Dany BaharPrithwiraj ChoudhuryHillel Rapoport

GLOBAL ECONOMY & DEVELOPMENT WORKING PAPER 134 | January 2020

Migrant inventors and the technological advantage of nations

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Migrant inventors and the technological advantage

of nations∗

Dany Bahar † Prithwiraj Choudhury Hillel Rapoport

The Brookings Institution Harvard Business School Paris School of Economics, Paris 1

Harvard CID, CESifo & IZA CEPII, CESifo & IZA

January 21, 2020

[Download Newest Version]

∗The authors are thankful to Ina Ganguli, Francesco Lissoni, Ernest Miguelez, andJames Sappenfield, as well as participants at the Wharton Conference on Migration, Orga-nizations, and Management (2019). We also thank four anonymous referees for commentsand suggestions. All errors are our own.

†Corresponding author: 1775 Massachusetts Ave NW. Washington DC, 20001. E-mail:dbahar@brookings.edu

1

Abstract

We investigate the relationship between the presence of migrant

inventors and the dynamics of innovation in the migrants’ receiving

countries. We find that countries are 25 to 60 percent more likely

to gain advantage in patenting in certain technologies given a twofold

increase in the number of foreign inventors from other nations that

specialize in those same technologies. For the average country in our

sample, this number corresponds to only 25 inventors and a standard

deviation of 135. We deal with endogeneity concerns by using historical

migration networks to instrument for stocks of migrant inventors. Our

results generalize the evidence of previous studies that show how mi-

grant inventors "import" knowledge from their home countries, which

translates into higher patenting. We interpret these results as tangi-

ble evidence of migrants facilitating the technology-specific diffusion of

knowledge across nations.

Keywords: innovation, migration, patent, technology, knowledge

JEL Classification Numbers: O31, O33, F22

2

"Through the ages, the main channel for the diffusion of

innovations has been the migration of people."

(Cipolla, 1976, p. 121)

1 Introduction

It is a known fact that German and Austrian Jewish scientists and inven-

tors who fled from Nazi Germany during the mid 1930s played a crucial role

in boosting the innovation capabilities of the countries that received them

and, in particular, of the United States. Moreover, this boost in innovation–

which was a result of higher combined patenting activity for both immi-

grants and natives–was in research fields (such as chemistry) where Ger-

man and Austrian scientists were active inventors in their home countries

prior to the war (Moser et al., 2014). While there is plenty and grow-

ing evidence of the impact of migration on innovation (e.g., Kerr, 2008;

Agrawal et al., 2008; Hunt and Gauthier-Loiselle, 2010; Kerr and Lincoln,

2010; Freeman and Huang, 2015; Ganguli, 2015; Bosetti et al., 2015; Choudhury,

2016; Akcigit et al., 2017; Breschi et al., 2017; Bernstein et al., 2018; Miguélez,

2018; Choudhury and Kim, 2018; Doran and Yoon, 2019)1, there is less sys-

tematic evidence–based on a larger number of countries examined over a

period of time–documenting the role migrant inventors and scientists play

in their receiving countries in terms of boosting innovation in those same

technologies. This paper attempts to fill this gap in the literature.

In particular, we ask: Do migrants boost patent production in their coun-

tries of destination (alt. origin) in the same technology classes in which their

home (alt. receiving) countries specialize? We find that, for any given coun-

try c and technology p, a twofold increase in the number of migrant inventors

from other nations that specialize in patenting in technology p is associated

with a 25% to 60% increase in the probability that c gains global technolog-

ical advantage in p within a decade. In our exercise, gaining technological

advantage implies achieving a number of patent applications in technology p

that is proportionally larger than the global average. This twofold increase,1See Lissoni (2018) for a comprehensive survey of this literature.

3

for the average country in our sample corresponds to only about 25 inven-

tors (and a standard deviation of 135). Our research question builds on the

findings by Bahar and Rapoport (2018) –who claim that migrants induce

industry-specific productivity shifts (as measured by export dynamics)– by

investigating the link between migrant inventors and innovation dynamics

as one plausible mechanism driving their results.

Our paper attempts to integrate two previously disconnected yet impor-

tant strands of the innovation and patenting literature: the literature on

comparative patenting across countries2 and the literature documenting the

role of migrant inventors in facilitating knowledge production across bor-

ders. The importance of geographic and political borders for knowledge

transfer and production has been long studied in the patenting and inno-

vation literature. Building on the rich literature of geographic localization

of knowledge spillovers (Thompson and Fox-Kean, 2005; Henderson et al.,

2005), Singh and Marx (2013) find a strong role of political borders in knowl-

edge diffusion: The authors find both country and state borders to have inde-

pendent effects on knowledge diffusion beyond what just geographic proxim-

ity in the form of metropolitan collocation or shorter within-region distances

can explain. In this literature, Foley and Kerr (2013) find that increases in

the share of a firm’s innovation performed by inventors of a particular ethnic-

ity are associated with increases in the share of that firm’s affiliate activity

in countries related to that ethnicity. Almeida et al. (2014) study patent

data and find that the utility of ethnic knowledge and collaborators depends

on the level of inventor embeddedness in the community. In a recent paper,

Kerr and Kerr (2018) connect collaborative patents to the ethnic composi-

2For instance, Glismann and Horn (1988) present an analysis of invention performance,as measured by patenting activities of six countries (France, Italy, Japan, United Kingdom,USSR, West Germany) relative to the United States for 41 SIC industries over 1963-1983,suggesting the existence of "catching-up" processes in terms of patenting activity. Morerecently, De Noni et al. (2018) assert that less innovative European regions (referred toas ’lagging-behind regions’ in their paper) must actively work to reduce the gap betweenthem and knowledge-intensive regions. The authors employ a seven-year panel dataset(2002–2008) using patent data at a regional level to validate the hypothesis that collab-orations, and specifically with highly innovative regions, positively affect the innovationperformances of lagging-behind regions.

4

tion of the U.S. inventors and cross-border mobility of inventors within the

firm. In another recent study, Berry (2018) studies global patent production

within multinational firms and finds that “knowledge network embeddedness”

with the headquarters, host country, and other countries increases future

patent production for MNEs. Choudhury and Kim (2018) exploit a natural

experiment and supply shock of Chinese and Indian migrant inventors in the

U.S. to find that ethnic migrant inventors are instrumental in transferring

contextual knowledge (i.e., knowledge locked in geographic regions), such as

the knowledge of herbal medicine, across borders.

Our paper finds a robust pattern of migrant inventors impacting cross-

country innovation dynamics for particular technologies with rich patenting

activity in their home countries prior to their move. In that sense, our

findings generalize some of the important findings by Moser et al. (2014) on

the spike of innovation in chemistry-related fields due to the inflow of Jewish

scientists and inventors to the U.S. in the early 1930s (summarized above);

as well as findings by Bernstein et al. (2018), who study patenting behavior

of immigrant inventors to the United States in recent decades and find that

these inventors tend to "import" foreign technologies into the U.S. (which

they measure by the higher propensity of these migrant inventors to cite

foreign patents and to work with foreign inventors). Beyond studies that

focus on particular countries or historical episodes, our paper–to the best

of our knowledge–is the first to use contemporaneous data to establish at a

global scale that migrant inventors do shape technology-specific innovation

dynamics. This is what we consider the main contribution of our study.

We arrived at these findings by linking and analyzing several sources of

data for 95 countries around the globe. First, we use data from the OECD on

patenting activity reported by the United States Patenting Office (USPTO)

for 651 technology subclasses as defined by the International Patenting Clas-

sification (IPC). Our focus on particular technologies is aligned with a rich

prior literature in innovation that has used classification of patents according

to technologies to study knowledge relatedness and technological distance

between countries (e.g., Jaffe, 1986, 1989; Breschi et al., 2003).3 To our3Specifically, Breschi et al. (2003) employ a measure of knowledge-relatedness, using

5

measure of innovation based on patent classification, we incorporate data on

bilateral stocks of migrant inventors compiled by Miguelez and Fink (2017),

which measures the presence of foreign inventors in every host country. As

will be described in detail later, we use these data to study the relationship

between the international mobility of inventors and the spike in patenting

activity in their receiving countries, in particular technologies in which their

home countries have a technological advantage. To measure this, we em-

ploy the Revealed Technological Advantage (RTA) measure, based on Soete

(1987). For each country, technology, and year in our sample, we quantify its

RTA and use it to measure the yearly intensity with which a country special-

izes in a given technology. For any technology, an RTA above 1 implies that

the inventors in a country in a given year filed proportionally more patents

than the world as a whole.

Using two decade-long periods (1990-2000 and 2000-2010), our exercise

looks at two different outcomes to measure the dynamics of specialization of

a country. First, we construct a binary variable that takes the unit value if

a country-technology pair achieved an RTA of 1 or more in a period of 10

years, conditional on that country having started off the decade with zero

patent applications in that same technology. We refer to this phenomenon

as a technological "take-off." Second, in order to study accelerations, we cal-

culate the decade-long growth rate in the number of patent applications for

each country-technology pair (which naturally is defined only for country-

technology pairs with some patent activity in the baseline period).4 We then

proceed to explore the extent to which the presence of migrant inventors from

(alt. to) countries that have a technological advantage in a specific techno-

logical class explains the take-off and acceleration of that same technology

in their receiving (alt. sending) countries over the course of the following

co-classification codes contained in patent documents, and examine the patterns of tech-nological diversification of the whole population of firms from the United States, Italy,France, UK, Germany, and Japan patenting to the European Patent Office from 1982 to1993.

4These two dependent variables are consistent with some of our previous work focusedon measuring dynamics of comparative advantage based on international trade data (e.g.,Bahar et al., 2014; Bahar and Rapoport, 2018; Bahar et al., 2019).

6

decade.

In order to deal with endogeneity concerns arising from migrant inven-

tors choosing their destination based on private information, the existence

of previous trends on technology-specific patent production, or the presence

of any other omitted unobservable variable that could bias our estimates,

we make use of two sets of instrumental variables (IVs): 30-year-old historic

migrant networks as well as the predicted number of migrant inventors based

on push and pull factors.5 We use these two variables, separately, to instru-

ment for the presence of inventor migrants from the same nationalities. In

order to rule out the possibility that our results are being driven by prior

trends not related to the actual presence of migrant inventors, we perform

a number of falsification tests that make our main results disappear. We

complement this with a number of additional robustness tests to deal with

possible alternative explanations to our results, which we explain in detail

below.

The rest of the paper is structured as follows: Section 2 outlines our em-

pirical strategy; Section 3 summarizes our main results; Section 4 conducts

subsample analysis and summarizes results from several robustness checks;

Section 5 concludes. There is also an Online Appendix that accompanies the

paper.

2 Empirical strategy

2.1 Research question

We investigate the relationship between international migration flows and

the dynamics of the innovation in migrants’ receiving and sending countries.

This question follows a research agenda exemplified in Bahar and Rapoport

(2018), which explore the role of migration–generally defined–on compara-

tive advantage dynamics using exports data. The main conclusion from that

study is that migrants serve as drivers of knowledge diffusion, which is re-

5As will be explained in detail later, our second IV approach is based on Card (2001)and constructed along the same lines as Burchardi et al. (2018) and Burchardi et al.(2019).

7

flected in the ability of countries to become significant exporters of the same

goods that the migrants’ countries of origin specialize in.

The results by Bahar and Rapoport (2018), suggestive of migrant-driven

knowledge diffusion, lack specificity in terms of the underlying channels

through which this process occurs. In this study, we shift our focus to in-

novation dynamics and the role that migrant inventors–a particular subset

of high-skilled migrants–play in it. We are interested in whether countries’

ability to innovate in specific technologies (without prior patenting activity)

is influenced by the presence of migrant inventors. Specifically, we ask the

following question: Can migrants induce patenting activity in their receiv-

ing (alt. sending) countries in the same technologies that their home (alt.

destination) countries have an advantage in? For the sake of better under-

standing, let us use a simple example. Suppose there are two countries in

the world: Israel (a country that specializes in patenting water technologies)

and Chile (a country that specializes in patenting mining technologies). The

analogous question then becomes whether the presence of more Israelis in

Chile can explain its specialization in water technologies and whether this

same presence is also associated with the ability of Israel to specialize in

mining technologies, as measured by patent applications.

2.2 Main data sources and sample construction

Data on patent applications (which we also refer to as patent production

throughout the paper) come from the OECD Stat database (OECD, 2014).

It counts all patents applications registered by the U.S. Patent and Trade-

mark Office (USPTO) by country of the inventor(s). The count disaggregates

the number of patents for each technology subclass based on the International

Patent Classification (IPC). An IPC subclass is defined by four characters,

letters, and numbers. Throughout the paper, whenever we refer to a tech-

nology, we are referring to an IPC subclass (which we often refer to as IPC

code). The original dataset covers patenting of 121 countries, and it extends

from years 1976 to 2011. The assignment of patents to countries is based

8

on the declared residence of the inventor(s) of the patent.6 The dataset also

includes figures for patents granted by the USPTO, also per country and

IPC code; as well as all patent applications to and granted by other patent

offices or treaties, such as the European Patent Office (EPO) and the Patent

Cooperation Treaty (PCT), which we also incorporate in our analysis for

robustness checks.

Our baseline specification, however, uses patent applications to the USPTO

unless otherwise noted, given its more ample coverage of patenting activity.7

In fact, Figure 1 plots the number of patent applications by year and source

for all three–USPTO, EPO, and PCT. The figure shows that the USPTO

accounts for a significantly larger number of patent applications than the

other sources, with about 300,000 applications in 1990, 600,000 in 2000, and

nearly 900,000 in 2010. While EPO is the second-largest source of patent

applications for years 1990 and 2000, in our sample, PCT overpasses it in

year 2010.

[Figure 1 about here.]

We also limit our main results to patent applications, as opposed to

granted patents. This is because a patent is typically granted a few years

after the invention actually happened. Hence, patent applications better fit

our purposes of measuring production of innovation in a given year. This

is consistent with the data, as portrayed in Figure 2 plotting total USPTO

patents applications and grants for years 1990, 2000, and 2010. As expected,

the number of applications surpasses the number of grants.

[Figure 2 about here.]

Our second main source of data is bilateral international stock of inven-

tors compiled by Miguelez and Fink (2017). The dataset measures for every

6For the (relatively few) cases of global collaborative patents (e.g., patents with inven-tors residing in different countries), the dataset assign a patent to each one of the countriesof the inventors.

7In Online Appendix Section G we present results using EPO and PCT figures thatare robust to our baseline estimations.

9

pair of countries, c and c′, the number of patents by inventor from coun-

try c′ in country c and vice versa. It is based on patent applications filed

under the PCT and has data for about 200 countries. The figures in this

dataset are an imperfect measure of the stock of foreign inventors in each

country by nationality of the inventor and year. They are imperfect because

the number of inventors is contingent on their patenting activity. In other

words, it could count the same foreign inventor multiple times or, on at the

other extreme, ignore her in a given year. For instance, if a foreign inventor

living in country c files two patents in year t, she would be double counted.

However, if a foreign inventor living in country c has patenting activity in

years t− 1 and t+ 1 but not in year t, then she would not be accounted for

in the data in year t. To overcome this possible fluctuation, we compute the

average stock from 1981 to 1990 of inventors living in each country c from

each country c′ as our measure for 1990, and we computer the average stock

from 1991 to 2000 for our measure of inventor migrants in year 2000. While

this is not a perfect solution, the average would not be driven heavily by

particular outliers in the data. Despite these important caveats, we refer to

these numbers throughout the paper as the stock of inventor migrants.

We include in our main dataset other bilateral measures to use as baseline

controls: FDI stocks as well as data on bilateral trade. The FDI data comes

from the OECD Stat database and tracks FDI flows to or from OECD mem-

ber countries (thus, it also reports FDI for non-OECD as long as it is to or

from an OECD partner).8 Using these data, we compute FDI stocks for the

periods 1985 to 1990 and 1991 to 2000. We also use bilateral trade data that

come from UN Comtrade with corrections implemented by Hausmann et al.

(2014). With this dataset, we compute stocks of bilateral trade for the pe-

riods 1985 to 1990 and 1991 to 2000 to be used as baseline controls. Both

the FDI and trade flows are deflated using the U.S. GDP deflator (base year

2000) from the World Development Indicators (WDI) by the World Bank

8The FDI data by the OECD includes all financial flows that are cross-border trans-actions between affiliated parties (direct investors, direct investment enterprises and/orfellow enterprises) recorded during the reference period. The main financial instrumentcomponents of FDI are equity and debt instruments.

10

before being transformed into stocks.9

We complement our dataset with overall bilateral migration from Ozden et al.

(2011), which we use as part of our identification strategy. The migration

dataset consists of total bilateral working age (25 to 65 years old) foreign-

born individuals for years 1960, 1970, 1980, 1990, and 2000.

The final sample resulting from merging all the different datasets de-

scribed above includes figures on patent applications and on migrant inven-

tors for 95 countries across 651 different technology subclasses (i.e., four-

character IPC codes). The list of countries with relevant statistics is pre-

sented in Online Appendix Section A. The final number of countries is a

result of limiting the sample to only countries with some patenting activity

in any technology subclass and any presence of migrant inventors. In order to

measure decade-long changes in patenting activities for country-technology

pairs, we define two decade-long periods (1990-2000 and 2000-2010) for the

analysis.

2.3 Empirical strategy

The aim of the paper is to study dynamics in patent production by a country

in a particular well-defined technology subclass (measured by patent appli-

cations) as a function of the presence of foreign inventors from countries that

specialize in that same technology. To do so, we need a measure to quan-

tify the extent to which a country specializes in a particular technology. Our

choice is the Revealed Technological Advantage (RTA) index, based on Soete

(1987) which, in turn, is analogous to the Revealed Comparative Advantage

(RCA) index by Balassa (1965) that is used in international trade.

We compute the RTA for each country and technology subclass in a given

year as follows:10

9We use 1985 as the lower limit for calculating these stocks given source data limita-tions.

10This is analogous to how RTA is computed in the dataset by OECD (2013), thoughwe construct the index ourselves.

11

RTAc,p ≡

patentsc,p/∑p

patentsc,p

∑c

patentsc,p/∑c

∑p

patentsc,p,

where patentc,p is the number of patent applications by inventors in coun-

try c in technology subclass p. This is an annual measure. For example, in

the year 1990, about 3.25 percent of all patents applications by Austrian in-

ventors belonged to technology subclass A63C, which corresponds to "Skates,

skis, water-shoes; roller skates; courts; and rinks." Overall, patent applica-

tions that year in that same technology by inventors from all over the world

represented 0.14 percent of all patents applications. Hence, Austria’s RTA

in technology A63C in the year 1990 was RTAAUT,A63C = 3.25/0.14 ≈ 23.

This means that inventors in Austria patent 23 times more in technology

A63C than the world as a whole.

We believe using RTA to measure patenting intensity is proper for several

reasons. First, RTA allows us to measure the specialization of one country in

a particular technology with respect to the rest of the world, not with respect

to another single country. Second, our measures of patent applications are

all based on a single patent agency and, thus, the numbers are comparable

across countries and years. Third, similar to Balassa’s RCA, the benchmark

value of 1 and above has an intuitive meaning, as can be understood through

the above example.

To study the question at hand with our sample, we follow the empirical

specification by Bahar and Rapoport (2018) and estimate:

Yc,p,t→T = βim

∑

c′

inventorsimc,c′,t × Rc′,p,t + βem

∑

c′

inventorsemc,c′t × Rc′,p,t

+ βFDI

∑

c′

FDIc,c′,t × Rc′,p,t + βtrade

∑

c′

tradec,c′,t × Rc′,p,t (1)

+ γControlsc,p,t + αc,t + ηp,t + εc,p,t ,

where c represents a country, p represents a technology subclass (i.e.,

IPC code), and t is a time subscript (T is another time subscript such that

T > t). The definition of the dependent–or left-hand side (LHS)–variable

Yc,p,t→T , changes with the estimation of different outcomes that measure

12

changes in the intensity of patenting of a country in a given technology. The

first outcome we use is a binary variable, which we refer to as a technological

"take-off. It measures cases when a country with no patent applications

whatsoever in a given technology at time t gains technological advantage in

that same technology at time T = t+ 10. In this case, we define Yc,p,t→T as

a binary variable equal to 1 if the number of patent applications in country

c and technology p results in having an RTA of 1 or more in the period of

time between t and T , conditional on having zero patent applications in that

same technology at the beginning of the period. That is:

TakeOffc,p,t→T = 1 if patentsc,p,t = 0 and RTAc,p,T ≥ 1.

We impose two additional conditions on our take-off measure to prevent

our results from being driven by noise. First, the country-technology pair

under consideration must keep its RTA value above 1 for four years after

the end of the year T (e.g., have a minimum RTA of 1 during the years

[T, T + 5]). Second, the country-technology pair under consideration must

have had an RTA value equal to 0 during all four years before the beginning

of year t (e.g., have a maximum RTA of 0 during the years [t− 5, t]).

We alternate our LHS variable with a measure of growth in patent appli-

cations for every country and technology subclass from years t to T . In that

case, Yc,p,t→T is simply the annual compound average growth rate (CAGR)

in the number of patents in technology p granted to inventors in country c

from years t to T = t + 10, conditional on having more than zero patent

applications in that same technology at the beginning of the period. That

is:

CAGRc,p,t→T =

(

patentsc,p,Tpatentsc,p,t

)1/T−t

− 1 if patentsc,p,t > 0.

Our main variables of interest are denoted by∑

c′ inventorsimc,c′,t×Rc′,p,t,

and∑

c′ inventorsemc,c′,t×Rc′,p,t, where Rc′,p,t = 1[RTA ≥ 1]. These variables

can be interpreted, respectively, as the stock of immigrant inventors from

and of emigrant inventors to other countries (denoted by c’) at time t, that

13

specialize in the production of patents classified under technology subclass

p, as indicated by the dummy variable Rc′,p,t.

As controls, we also include the sum of the stock of FDI (inflows plus

outflows) and the sum of the stock of trade (imports plus exports), using

the same weighting structure as above. Including these controls allows us to

reduce omitted variable bias when estimating βim and βem. This is because

trade and/or capital flows with the same countries where the inventors come

from or go to (denoted as c′) could also explain innovation dynamics in

country c. Using the same weighting scheme allow us to control for the total

trade and FDI to those same countries c′. While ideally we would include

trade and investment from those countries that relate to each particular

technology p, such data is not available. Since our weighting procedure

accounts for total trade and FDI from those countries, they are inclusive of

flows that relate to particular technologies. Thus, we believe that measures

control for these plausible channels.11

In addition, we include country-year fixed effects, denoted as αc,t, to con-

trol for any country-level time-variant characteristics that correlate with both

national migration determinants and aggregate productivity levels, such as

income, size, institutions, etc. ηp,c represents technology-year fixed effects,

to allow for a different constant for each combination of year and IPC tech-

nology subclass.

We also include a vector of controls for baseline variables when measuring

using CAGR on the LHS: the baseline (initial) level of patent applications for

that same technology in that country, as well as the previous period CAGR

of patent applications in the same technology to control for previous trends.

To avoid undetermined lagged growth rates when the initial level of patents

is zero we add 1 to both the number of patents in the numerator and the

11Of course, there is a genuine discussion to have on whether these are, in fact, "badcontrols". In other words, if the inflow of inventors from countries that specialize ina certain technology subclasses triggers more trade and investment that in turn boostinnovation dynamics in the receiving country, then we would be underestimating ouroverall effect. However, we decide to keep them in the baseline specification as we areinterested in estimating βim and βem which would measure the partial correlation (ormarginal effect) regardless of trade and investment. However, in Online Appendix SectionE we show that our results are robust to excluding these controls.

14

denominator. Thus, we also add as a control a binary variable indicating

whether patentsc,p,t−10 = 0 (at the beginning of the previous period, i.e.,

1980 or 1990).

All level variables are transformed using the inverse hyperbolic sine (MacKinnon and Magee,

1990). This linear monotonic transformation behaves similarly to a log-

transformation, except for the fact that it is defined at zero. The interpre-

tation of regression estimators in the form of the inverse hyperbolic sine is

similar to the interpretation of a log-transformed variable.12 Thus, since our

LHS variables are not transformed using a logarithmic scale, the interpreta-

tion of the estimators are linear-log.

2.4 Identification

Our main goal is to get unbiased estimators for βim and βem. This is chal-

lenging, as one might expect that the choice of country for foreign inventors

might be correlated with dynamics of specialization in certain technologies.

In other words, there might be other country-technology-time characteris-

tics, perhaps unobservables, that can explain both the inflow and outflow

of inventors and dynamics of patent production. We try to overcome this

by estimating our specification using 2SLS, using two sets of instrumental

variables (IVs) for both immigrant and emigrant inventors (e.g., a set of IVs,

in our case, include two variables–one for each endogenous regressor).

First, for each country-technology-year combination, we instrument the

immigrant and emigrant inventors with the total stock, lagged by 30 years,

of immigrants from and emigrants to those same countries (e.g., we ap-

ply the same weights defined byRc′,p,t). In that sense, our instruments are∑

c′ immigrantsc,c′,t−30 ×Rc′,p,t and∑

c′ emigrantsc,c′,t−30 ×Rc′,p,t.

Second, we construct an additional set of IVs for the number of immigrant

and emigrant inventors using pull and push factors, computed with the data

itself, expanding the approach first introduced by Card (2001). Recently,

Burchardi et al. (2018) and Burchardi et al. (2019) use a similar approach

12The inverse hyperbolic sine (asinh) is defined as log(yi+√

(y2

i + 1)). Except for smallvalues of y, asinh(yi) = log(2) + log(yi).

15

to estimate the effect of immigration on FDI and innovation, respectively, for

the U.S. Our instrument is based on a prediction of the actual stock of inven-

tor migrants between c and c’, combining a "push" and "pull" component

as follows:

invIVc,c′,t = push−[c,c′]c′,t × pull

−[c,c′]c,t ×

∑

c

∑

c′

inventorsc,c′,t ,

where push−[c,c′]c′,t is the share of all migrant inventors in year t from coun-

try c′ to all countries, excluding from the calculation migrant inventors from

country c′ to country c in that same year. In other words, the share varies

at both c and c′ levels, because it purposely excludes migrant inventors from

c′ to c (in year t), as an additional step to reduce possible sources of endo-

geneity when using invIVc,c′,t to instrument for inventorsc,c′,t. While this

step adds an extra layer of complexity to the intuition of the instrument, we

believe it is important in order to construct a truly exogenous instrument,

as we are predicting the flow of inventor migrants between c and c’ without

using the information available for that country-pair. The pull−[c,c′]c,t compo-

nent represents the share of all migrant inventors in year t to country c from

all countries, following the same rule that excludes the actual flow of inven-

tors from c to c′ in year t. For clarity, the terms push−[c,c′]c′,t and pull

−[c,c′]c,t are

computed as follows:

push−[c,c′]c,t =

∑

i inventorsi,j,t − inventorsc,c′,t∑

i

∑

j inventorsi,j,t − inventorsc,c′,t

pull−[c,c′]c,t =

∑

i inventorsi,j,t − inventorsc,c′,t∑

i

∑

j inventorsi,j,t − inventorsc,c′,t

Finally, to construct our instruments, we apply the same weighting scheme

explained above using the predicted number of inventors between each pair

of countries c and c′ based on pull and push factors, as:∑

c′ invIVimc,c′,t−30×

Rc′,p,t and∑

c′ invIVemc,c′,t ×Rc′,p,t.

Both set of instruments, in order to be valid, should be able to explain

16

enough variation in the endogenous variables. We expect this to be the case

because historic migrant communities should work as a pull factor for the

decision of inventors to migrate to particular countries. In addition, the

predicted number of inventors based on push-pull should be a good enough

predictor of actual migrant inventor flows between countries, even when ex-

cluding the countries under consideration when constructing them. We find

this to be the case in our sample based on the reported first-stage statistics13

(with some exceptions, which are discussed thoroughly).14

In addition to the explanatory power of the first stage, for our instru-

ments to be valid (and, thus, to be able to interpret our 2SLS estimators

as causal), they need to comply with the exclusion restriction. In our case,

this exclusion restriction can be verbalized as follows: It must be technology-

specific production (e.g., patent applications) in any given country and not

correlated with our instruments, other than through the presence of inventor

migrants today. Furthermore, to be able to interpret our 2SLS estimators

as causal, we also must assume that countries do not engage in technology-

specific innovation agreements based on their historic migrant networks that

are not captured via FDI or trade (since we are controlling for those flows,

too).15

In the case of our first instrument, the assumption is that the existence

of a historic migrant community from country c′ in the destination country

explains the flow of migrant inventors, a particular subset of high-skilled mi-

13In all of our 2SLS estimations we report the Kleibergen-Paap F statistic to be used todetermine whether instruments are weak, which according to Stock and Yogo (2005), mustbe above 16.78 when using two endogenous variables and two instruments. We acknowl-edge that these critical values are not strictly usable in the case when we do not assumei.i.d., but for the most part, unless otherwise noted, our Kleibergen-Paap F statistics arehigh enough that there are no reasons for concern regarding weak instrumentation.

14Naturally, there could be concerns that the observed first-stage correlations betweenmigrant inventors and the instruments are artificially being driven by the weightingscheme. However, this is not the case: both instruments have a strong explanatory poweron current stock of migrant inventors before we apply the weights and transform theminto country-technology-year variables. We include evidence of this in Online AppendixSection B.

15Note that since we include trade and FDI in our 2SLS estimation (i.e., they are alsopart of the first stage) we already control for the fact that the instruments might affectfuture innovation through FDI and trade.

17

grants; but–at the same time–that historic migrant community from country

c′ does not explain future dynamics of patent production other than through

the inventor migrants who had arrived later. While we believe this is a rea-

sonable assumption, one might think that the presence of a historic migrant

community might affect the diffusion of knowledge through channels unre-

lated to the presence of migrant inventors (and, also, unrelated to trade and

capital flows with their countries of origin). If this is the case, then our

instrument would be invalid.

Therefore, we also present results with another instrument–the predicted

number of inventor migrants using push and pull factors–in order to further

validate our estimations. In the case of our second instrument and in order

to interpret our estimates as causal, our assumption is that push and pull

factors used to predict inventor migrant flows are not correlated to inno-

vation dynamics other than through the presence of the migrant inventors

themselves. Since we constructed the push and pull factors in a way that

excludes information about the country-pair under consideration, we believe

this assumption is reasonable. Note, too, that in this setting, the inclusion of

country-year fixed effects controls for the overall attractiveness of inventors

to the country under consideration, further reducing endogeneity concerns.

Thus, in a sense, we are exploiting mostly the push factor, which is more

likely to be exogenous to innovation dynamics for a country-technology pair.

In summary, we believe these are reasonable assumptions to make, though

we acknowledge there might still be weaknesses in our approach. Thus, to

complement our efforts in establishing the relationship, we also perform a

number of falsification tests showing that our results are indeed driven by

the flow of inventors and do not respond to previous trends or other variables

(observables or not) that are not accounted for in our main estimation.

2.5 Descriptive statistics

Table 1 presents descriptive statistics for our sample. Panel A presents the

summary statistics for the subsample that focuses on technology take-offs

(i.e., for all observations of c, p, and t for which RTA = 0), while Panel B

18

shows the same for the subsample focusing on growth of patent production

(i.e., for all observations of c, p, and t for which patentsc,p,t > 0).

[Table 1 about here.]

Panel A of Table 1 describes that the unconditional probability of a

take-off for the average country and average technology subclass, pooling

observations for two decades (1990 to 2000 and 2000 to 2010), is 2.2 percent.

Note that this is based on the sample limited to country-technology pairs

with zero patent applications at the initial year of each decade. Panel B

presents statistics based on the complementary sample; that is, with at least

one patent application at the beginning of each decade for every country-

technology pair. This sample is used to measure the impact of migration on

growth of technologies in terms of patent applications. The 10-year CAGR

for the average country-technology pair–also pooling observations for two

decades–is 0.7% and varies from -30% to 80% in both extremes for some

country-technology pairs. In this sample the baseline number of patent ap-

plications for the average country-technology pair is about 16.45. Notably,

the number of observations that make up the "technology take-off" sample

is almost six times as large as the sample described in Panel B. This is not

surprising, since the vast majority of country-technology pairs have, in fact,

no patent activity.

The tables also include figures for immigrant and emigrant inventors

weighted using the scheme used on the right-hand side of Specification (1).

According to Panel A, which focuses on take-offs, the average country-

technology pair in the sample has about 24 inventors who have immigrated

from countries that specialize in that same technology and about 80 inventors

who have emigrated to countries specializing in that technology. Those same

figures in the sample summarized in Panel B are about 710 and 610, respec-

tively. The larger numbers of average inventor migrants in Panel B responds

to the fact that such a sample is composed mostly by developed nations,

which host many more inventors (as those include only country-technology

pairs with some patent applications).

19

The table also summarize our IVs. The first set of instruments includes

the 30-year lagged stock of immigrants and of emigrants, weighted using

the same weighting scheme as our right-hand side variables of interest. The

average values for these figures are about 69,000 immigrants and 142,000

emigrants in Panel A and 499,000 immigrants and 449,000 emigrants in Panel

B. As expected, these numbers are significantly larger than the number of

inventor migrants, as inventors are only a very small subset of all migrants.

The second set of instruments include the predicted number of immigrant

inventors and emigrant inventors based on the pull and push factors, using

the same weighting scheme as the actual stock of immigrant and emigrant

inventors. In Panel A, the average figures for these IVs are 18 predicted

immigrant inventors and 55 predicted emigrant inventors, whereas in Panel

B, the corresponding statistics are 469 predicted immigrant inventors are 403

predicted emigrant inventors. The same reasoning as before with respect to

the larger average numbers seen in Panel B as compared to Panel A applies

in this case.

Finally, the table also has subsample statistics on total trade and FDI,

in billions of dollars, constructed using the same weighting scheme.

3 Main Results

The main question we aim to answer is whether a country can become a

significant innovator of a particular technology–what we call a technology

take-off–if it has immigrant inventors from (or emigrant inventors in) other

countries that specialize in patenting activity in that same technology. A

simple look at the raw data, represented in Figure 3, presents preliminary

evidence of that being the case. Average take-off rates of country-technology

pairs are higher whenever they host a larger number of inventor immigrants

from other countries that specialize in those same technologies, for both

periods 1990-2000 and 2000-2010. In particular, the figure shows the un-

conditional probability of a country-technology pair taking off in the period

1990-2000 is about 0.2% when it had a stock of immigrant inventors below

the median in the baseline year (1990), compared to 0.8%–about four times

20

as much–when the stock of immigrant inventors is above the median. For

the 2000-2010 period, the corresponding figures are 2.3% and 5.7%. The

figure also shows the same figures for decade-long growth rates for patent

applications. During the 1990-2000 period, country-technology pairs with

a stock of immigrant inventors (from other countries that specialize in that

technology) below the sample median grew at a pace of 0.24% a year, com-

pared to 1.08% a year for those with a stock of immigrant inventors above

the sample median. However, for the 2000-2010 decade, we see the opposite

pattern: country-technology with a stock of immigrants below the median

grew faster in terms of patent applications than country-technology pairs

with a stock of immigrants above the median.

[Figure 3 about here.]

Many confounding factors could explain Figure 3, of course. Therefore,

next, we present results using more rigorous estimation techniques.

3.1 OLS and 2SLS estimations

The estimation of Specification (1) is presented in Table 2. The upper panel

estimates the changes in the probability of technology subclass take-off as a

function of migrant inventors.16 In the estimations, as mentioned above, all

of the regressors have been transformed using the inverse hyperbolic sine and,

therefore, the interpretation of the coefficients correspond to semi-elasticities

(i.e., linear-log).17 The first three columns show results using OLS as the

estimation technique, whereas Columns 4 to 9 use 2SLS estimations, using

two set of IVs, as detailed in Section 2.3.

[Table 2 about here.]

16As explained above, we define a technology take-off as cases where a country achievesa RTA of one or more within a decade, starting off from no patenting activity (see Section2.3 for formal definition).

17We refrain from rescaling our right-hand side variables in terms relative to population.This is mainly because the inclusion of country-year fixed effects control for populationsize, reducing concerns that our results are driven by scale effects. Yet, the falsificationtests we present below also rule out the possibility of scale effects driving our results.

21

The results of Panel A estimate the partial correlation of our variables

of interest: immigrant inventors from and emigrant inventors in countries

specializing in a given technology subclass at the beginning of the decade

(separately in Columns 1 and 2 and jointly in Column 3) with respect to the

take-off of the same technology by a country.18 Results in Columns 1 and 3

show that a twofold larger stock of immigrant inventors from countries that

specialize in technology p is associated with an increase in the probability of

the receiving country specializing in patent applications in technology p of

0.51 percentage points. Given that the unconditional probability of a take-

off is 2.2 percent, this represents an increase in the probability of about 23%.

The estimator for emigrant inventors is not statistically different from zero

when jointly estimated with immigrant inventors (Column 3).

The economic significance of this number is quite large. Given that the

stock of immigrant inventors in the sample is about 24 people for the average

country-technology pair, and a standard deviation of 135 inventors, a twofold

increase implies a relatively small number of inventors. Thus, according to

our results, a small number of migrant inventors has significant and large

explanatory power on the likelihood the receiving country will gain advantage

in a new technology subclass (in which it had no patent activity beforehand).

Columns 4 to 6 in the upper panel of Table 2 replicate the results using

a 2SLS estimator; we use instruments based on the 30-year lagged stock of

immigrants from (and of emigrants in) the same countries as the migrant

inventors (IV1). Given that we have two instruments, we can use them in

estimations that include both variables of interest separately (Columns 4

and 5) as well as jointly (Column 6). Columns 7 to 9 present an alternative

18Our estimates are robust to using a maximum likelihood estimator given the binarydistribution of our dependent variable in Panel A (specifically, the complementary log-log method which is more appropriate for our setting, following Singer and Willett, 2009). It is also robust to using the methodology suggested by Horrace and Oaxaca (2006),to deal with the possibility of our results being driven by outliers. For more details, seediscussion in Online Appendix Section C. In addition, Online Appendix Section D presentsresults using alternative left-hand side variables, including a more widespread –and lessrestrictive– measure of take-off: a binary variable that takes the value of 1 whenever acountry-technology pair goes from zero patent applications to any number higher thanzero.

22

2SLS estimation using our second set of instruments: the predicted number

of inventors using contemporaneous push and pull components (IV2), as

explained in Section 2.4. Similarly, because this set includes two instruments,

we can estimate both variables of interest separately or jointly. Note that

the table reports the Kleibergen-Paap F statistics for all 2SLS estimations,

which are large enough to eliminate any concerns of weak instrumentation.

The 2SLS results are qualitatively similar to the OLS results, but higher

in magnitude by a factor of 2 to 3. Interestingly, the two different 2SLS esti-

mations, using very different instruments, yield strikingly similar point esti-

mates, reinforcing the validity of our identification strategy. It is somewhat

counterintuitive at first, when comparing the OLS and 2SLS estimations,

that the point estimate of βIM becomes larger after the instrumentation. If

anything, we would expect a positive bias in the OLS estimates, not a neg-

ative one, as unobserved forces that lead to more innovation might also pull

immigrant inventors (the opposite would happen for emigration, for which

the OLS coefficient is statistically indistinguishable from zero). But it could

well be that the 2SLS results correct for biases due to measurement error of

our endogenous variables (in fact, as explained in Section 2.2, these measures

likely suffer from measurement error).19 By in large, however, our 2SLS re-

sults are inconclusive when it comes to understanding whether the OLS bias

is positive or negative: Even though the magnitudes of the 2SLS estimates

are larger than that of the OLS, the standard errors have also increased and,

thus, we cannot reject the hypothesis that both estimates for βIM (as well as

βEM ) are statistically different, as shown in Figure 4.

[Figure 4 about here.]

Relying on the 2SLS estimates, the effect of a twofold increase in the

number of immigrant inventors results in an increase of 50% to 60% in the

19Since our regressors of interest are a compound variable (i.e., the aggregated numberof migrant inventors weighted by Rc′,p,t), the larger magnitude of the 2SLS estimates withrespect to OLS suggests our results are not driven by the second term (e.g., a convergenceeffect due to the "technological gap" between the countries), but rather by the first onewhich we effectively instrument for while keeping unchanged the weights. We thank ananonymous reviewer for making this point.

23

likelihood of the receiving country specializing in patent applications in the

same technologies in which the migrants’ home countries specialize.

Panel B of Table 2 estimates the partial correlation of our variables of

interest on the growth rate of technology-specific patent applications for the

average country, as a function of inventor immigrants from (and inventor

emigrants in) other countries that specialize in that same technology. The

sample we use is limited to those country-technology pairs for which the

initial value of patent applications is above zero, as it is not possible to

compute growth rates otherwise. But there is another, more fundamental,

reason. In essence, this distinction allows us to focus on innovation dynam-

ics for technologies already being patented in the country. In those cases,

arguably, there is already a critical mass of inventors with knowledge on

that specific technology subclass, as opposed to cases in which there is no

prior patenting activity whatsoever, such as country-technology pairs in the

subsample used in Panel A. The distinction between an extensive margin

(i.e., take-offs) and an intensive margin (i.e., growth) is often used in the

international trade literature when studying the composition dynamics of

countries’ exports baskets.20

The results from Panel B present a conclusion qualitatively similar to

that in Panel A. The OLS estimations (Columns 1-3) imply that, for the

average country, a twofold increase in the number of immigrant inventors

from other countries specializing in technology p explains a higher growth

rate in patenting activity of the same technology p of 0.34 to 0.45 percentage

points per year over the following decade. Also, according to the OLS results,

we find that a twofold increase in the number of inventor emigrants in other

countries is correlated with annual growth rates in patent activity that are

higher by 0.29 to 0.42 percentage points for the technologies of specialization

of the receiving countries. Since the decade-long unconditional growth rate

20In Online Appendix Section D2 we use a measure of growth in patent applicationsthat is defined for country-technology pairs with initial value of zero (i.e., a symmetricpercentage change). With this growth measure as the dependent variable we are able toestimate our empirical specification on all the sample and find robust results; though theresults are driven by the observations with no initial patenting activity. Our results usingthis aggregated measure, however, are statistically weaker.

24

for patent applications is 0.7% (see Panel B, Table 1), then the marginal

effect for immigrant inventors according to Columns 1-3 corresponds to an

increase of up to 65%. Note that in this sample, however, the average number

of immigrant inventors is much larger and corresponds to about 700.

Columns 3-9 estimate the same specification using 2SLS, with the two

sets of instruments discussed above. However, across all columns, the first-

stage statistics are not nearly as large as the ones in Panel A, implying that

there could be a weak instrumentation that makes all the 2SLS estimations in

Panel B results invalid and, therefore, we cannot solve endogeneity concerns

for this sample.21 In fact, as opposed to Panel A, the 2SLS point estimates

are quite inconsistent across the different estimations. Therefore, while our

findings on technology take-offs seem quite robust, we cannot make any

claims about growth rates.

The table also reports estimators for our control regressors, namely FDI

and trade. The idea behind including these controls is that they could very

well correlate with the flow of migrant inventors and, in turn, could explain

future innovation dynamics. The point estimates are volatile around the zero

value (seldom statistically significant), across the different specifications in

both panels. Their inclusion in our model is part of our identification strategy

to reduce concerns of biases in our regressors of interest, βim and βem. The

lack of a clear relationship between these controls and the dependent variable

is intriguing. However, there is a mechanical and straightforward reason

for this (lack of) result: the high multicollinearity of these terms with the

country-year fixed effects. Despite the fact that they vary across technology

subclasses within each country and year, in practice such variation is very

small. Note that the regressors are the sum of all trade and FDI to and from

countries where migrant inventors are from or in for that particular year.

Since most trade and FDI happens between developed nations, which is also

where most of the innovation happens across a wide variety of technology

subclasses, these terms are very similar to–and, in fact, their correlation is

above 0.8 with– total unweighted trade and FDI figures for every country

21Note that the difference in the explanatory power of the instrument could be expected,as the samples used in both panels are very different

25

and year (which are, by definition, perfectly multi-collinear with country-year

fixed effects). Therefore, the inclusion of country-year fixed effects eliminates

most of the variation needed for properly estimating a partial correlation

between these controls and the dependent variables. This is consistent with

the fact that we find strikingly similar results if we exclude these controls

(see Online Appendix Section E). But, even if there was no multicollinearity

problem, the estimation of these controls could result in very small estimates.

This is because, plausibly, only a small share of both trade and capital flows

(both of which aggregate inflows and outflows) is relevant for innovation

and, in particular, for innovation of specific technologies. Ideally, we would

have preferred to use more specific controls, such as trade and capital flows

relevant for the particular technology under consideration. However, not

only is that data not readily available, but it is not clear how to construct

such measures, if it is possible at all. In order to be conservative, however,

we choose to include them in our baseline specification, but refrain from

concluding anything about these flows in our empirical setting.

All in all, our main results support the idea that migrant inventors facili-

tate the spread of ideas reflected in significant patenting activity in technolo-

gies in which their home countries’ specialize. While in the main body of the

paper we use patent applications as our main data source, Table G3 in the

Online Appendix (Section G) shows that our main results are robust to using

granted patents as opposed to patent applications. Naturally there could be

important gaps between the time of the patent application and the time of

its acceptance by the USPTO, and that time gap could be problematic in

our setting. However, the fact that the results are robust to using granted

patents is complementary to our main estimation, as it provides suggestive

evidence that the process through which immigrant inventors affect the dy-

namics of patent applications for a given technology is also reflected in the

ability of the receiving country to convert some of those applications into

granted patents.

26

3.2 Falsification tests

While we believe our estimation methods outlined in the previous section

deal, for the most part, with plausible endogeneity concerns between the

flow of inventor migrants and countries’ patenting activity, we acknowledge

that there could be violations to the exclusion restriction of our instrumental

variable approach. The nature of our macro-level dataset, while is useful to

generalize our findings, also poses important challenges to our ability to

perfectly identify the relationship we are studying.

In order to deal further with some remaining endogeneity concerns, we

propose two tests to explore whether our results are indeed consistent with

the possibility of inventor migrants impacting patenting activity of their

receiving countries or, alternatively, they are driven by trends in the data,

unrelated to migration, for which we are not accounting. For this purpose,

we perform two falsification tests by altering the right-hand side variables

of interest. While not a perfect approach for identification purposes, we

consider it useful to show that our results do respond to actual variation

in migrant inventors and not to existing pre-trends or other factors omitted

from the analysis.

First, we replicate Specification (1), but this time using the weighting

parameter Rc′,p,t = 1 if RTAc′,p,t = 0. That is, we exploit variation in

inventors migrating from and to countries c′ that had zero patent applications

in technology p at time t. Table 3 reports the results.

[Table 3 about here.]

When alternating the right-hand side variables of interest in this way, we

find that the results are very different than the ones presented in Table 2.

According to Panel A, the presence of migrant inventors from or in countries

with no patent applications in technology p does not consistently explain an

increase in technology take-offs as compared to our baseline results (Columns

1-3 present OLS estimators and Columns 4-9 present 2SLS estimators us-

ing the two sets of instruments). If anything, the negative point estimates

suggest that immigrant inventors from and emigrant inventors to countries

27

that do not patent at all in a specific technology reduce the likelihood of a

country patenting in that same technology during the following decade. The

results in Panel B, which focus on growth, are consistent with those in Panel

A (note, though, that for the 2SLS estimates, we have a weak first stage)

and show that country-technology pairs grow slower in terms of patent ap-

plications whenever they host immigrant inventors from other countries that

do not patent in those same technologies.

Second, we alternate our right-hand side variables of interests based on

number of inventors that are randomly generated and, therefore, do not

reflect the actual number of inventors. In that context, we present results

for two models. The first approach, which we title "Random Model 1"

randomizes the number of inventors between countries without altering the

distribution. In other words, we "reshuffle" the number of migrant inventors

between countries randomly, such that the total global figure for (random)

migrant inventors in a given year is the same as the actual number. Thus,

the distribution and the average for these two variables are exactly the same.

The second approach, referred to as "Random Model 2" does not impose

any restrictions whatsoever and simply creates a fake number of migrant

inventors based on a random number (from 0 to 1, uniformly distributed) to

every country pair and year.

Our exercise is based on 500 iterations, which involve reconstructing the

dataset each time (e.g., recomputing the actual and random inventor vari-

ables by assigning the weighting structure and then collapsing the dataset

to create aggregated figures for every country, technology, and year cell).

Figure 5 presents density plots of correlation coefficients between the actual

number of inventors and each one of the 500 iterations, for both models.

Note that these correlations are computed using just country-pair data, be-

fore applying the proper transformations to construct the regressors detailed

in our empirical specification.

[Figure 5 about here.]

Note that when the randomization is done in a way that preserves the

same distributional characteristics of the original variable (left panel in Fig-

28

ure 5), the correlation between the random and actual variable always takes

positive values across the 500 iterations, and its distribution is character-

ized by having a fat-tail. However, when not imposing any restrictions on

the generation of a random number (right panel), the correlation coefficients

between the random and actual figures for the 500 iterations distribute quasi-

normally and around zero, as is expected when correlating with a truly ran-

domly generated variable. Despite this fundamental difference, we present

results using these two approaches and find consistent results.

Figure 6 summarizes our results (one marker for each one of the 500 it-

erations) when using OLS to estimate our main specification but substituting

the main variables of interest∑

c′ inventorsimc,c′,t×Rc′,p,t and

∑

c′ inventorsemc,c′,t×

Rc′,p,t with the ones that we constructed through randomization for inven-

tor immigrants and emigrants. Note that the reported estimators for βIM

and βEM are a result of including both regressors simultaneously in the re-

gression (analogously to Columns 3 of Table 2). The figure also reports the

estimator from our baseline specification, using the actual number of migrant

inventors, as reported in Table 2 and denoted by a diamond-shaped marker.

Whiskers in the figure represent 95% confidence intervals.

[Figure 6 about here.]

Clearly, the results using a random number of inventors across the 500

iterations is extremely noisy in both models and both estimators (immigrants

and emigrants). In fact, when focusing on estimates of βIM , in nearly 45%

of the iterations, the result is not statistically different from zero for Random

Model 1 (despite the fact that the correlation between the actual and random

number of migrants is always positive in this model). The corresponding

figure for Random Model 2 is just above 60%. When it comes to βEM , for

which the OLS estimator using the real number of inventors is statistically

insignificant itself, the estimators based on a random number are statistically

insignificant for about 85% of the 500 iterations in the first model and close

to 70% in the second model.

These two falsification tests are important, as they should alleviate any

remaining concerns that our results are being driven by spurious correlations

29

or previous trends. The results are also useful to alleviate concerns that our

results are being driven purely by scale effects. This is particularly relevant

when focusing on Random Model 1, which keeps unchanged the aggregated

scale global flows of migrant inventors.

4 Supplementary analysis

4.1 Heterogeneity of results

In order to study the relationships documented above in more detail, we rees-

timate Specification (1) across different subgroups of our sample. We do this

to understand whether there are different trends across several dimensions

and also to explore whether a particular set of observations in the sample is

driving the observed overall results. Table 4 summarizes this exercise.

[Table 4 about here.]

The left panel of Table 4 reports OLS estimates both for βim and for βem,

while the two other panels report the 2SLS estimates for the same regressors

using both set of instruments (as in the previous section). The table presents

results of the specification that uses technology take-offs as the dependent

variable (thus, country-technology pair observations are limited to having

an initial number of granted patents equal to zero). The estimates reported

for βim and for βem are based on a specification that includes both of their

corresponding regressors simultaneously. The first row uses all observations

(the same sample as presented in the upper panel of Table 2).

The rest of the rows present results for different cuts of the sample. Across

the board, based on the 2SLS estimates, we find that our results typically

hold only for immigrant inventors, not for emigrants, consistent with our

findings so far.

Additionally, our results are being driven by both OECD and non-OECD

countries alike.22 Also, the results are particularly driven by the period

22We count countries in our sample as OECD members of only if they had been suchprior to the first period studied (e.g., the classification does not count countries as OECD

30

2000-2010, the decade for which most of the patenting activity in our sample

occurs.

Finally, we divide our sample into eight IPC sections (which correspond

to the first "character" of the four-character IPC subclasses used throughout

the paper). These are human necessities (A); performing operations and

transporting (B); chemistry and metallurgy (C); textiles and paper (D); fixed

constructions (E); mechanical engineering, lighting, heating, weapons, and

blasting (F); physics (G); and electricity (H). While our OLS results do show

some heterogeneity in the statistical significance of the results for βim, when

it comes to the 2SLS results, we do not find any particular technology section

driving the results. In particular, using the first set of instruments, we do

not find evidence that our aggregate results are driven by any particular

technology. This is almost consistent with the results using the second set

of instruments where, except for two technologies (fixed constructions and

performing operations), we again find that our results are not driven by any

technology class in particular.

4.2 Further robustness tests

We perform a number of supplementary analyses for robustness purposes,

which we briefly discuss in this subsection. We provide more details in the

Online Appendix.

Our right-hand side variables measuring migrant inventors, trade, and

FDI are highly multi-collinear, which could raise concerns about our model

being misspecified. However, all of our estimations include a large number of

fixed effects, so our estimations correct for different scales. Additionally, we

have computed the variance inflation factor (VIF) for our main OLS estima-

tion (Column 3 of Table 2, which include technology-by-year and country-by-

year fixed effects) to assess the availability of enough independent variation

among correlated variables. The mean VIF value is 1.31, which is within

the acceptable range. Therefore, multicollinearity does not appear to be an

members if they became so during the 1990s or the 2000s). For a complete list of countriesin the sample, including which ones we categorize as OECD members, see Online AppendixSection A.

31

issue.

Also, we reestimate the specification estimating the impact of migrant

inventors on technology take-offs based on patent applications using a non-

linear estimation, as it is often done for binary outcomes. In particular, we

implement the complementary log-log estimator, which is a better estima-

tor than logit or probit if the probability of take-off is small (e.g., there are

many zeros), as is our case (see Singer and Willett, 2009). We also apply

the methodology by Horrace and Oaxaca (2006) that corrects for predicted

values of the dependent variable outside the 0 to 1 range. For more details,

see Online Appendix Section C.

We also find that our results are robust to variations of our left-hand side

variables. In particular, it is robust when using a binary variable that does

not depend on RTA, as well as a growth rate that is defined when the initial

value is zero (allowing us to estimate using the sample without splitting it

into two subsamples). See Online Appendix Section D for more details and

the results.

Additionally, in Online Appendix Section D, we document a number of

other consistent findings. Our results are robust to using patent application

data based on the European Patent Office (as opposed to the USPTO) and

find results that are qualitatively and quantitatively similar (see Table G1).

This robustness test is particularly important because it shows that our

results are likely not driven by the "home advantage" effect, given that such

bias would naturally be different for USPTO or EPO patents (Criscuolo,

2005).23 Our results are also robust to using patent application figures based

on the PCT, which we also document (see Table G2); this is also the main

source out of which the migrant inventor numbers come. We also replicate

our results using data on granted patents (as opposed to patent applications)

according to the USPTO (see Table G3). While granted patents typically

involve an important time gap, we still believe it is relevant to show that

23An alternative approach could have been to limit our sample to OECD triadic patents,but this raises a number of other difficulties in terms of possible biases, given that–bydefinition–these patents represent a subsample of all patents, and it is unclear –at firstsight– what are the biases driving the selection of patents into that group.

32

our results are robust to using this measure, as a granted patent is indeed

a confirmation that the innovation is novel enough. In the context of our

results, this is crucial; it suggests our results are not only driven by an uptake

on filling patents, but on actual innovation.

We also explore the possibility that our results are driven by intellectual

property theft practices (along the lines of the evidence on industrial espi-

onage by Glitz and Meyersson, 2017). In other words, we look at whether

inventor migrants facilitate the spread of technologies through stealing in-

tellectual property (IP) rather than through knowledge diffusion. To some

extent, our results–particularly the ones using granted patents in Online Ap-

pendix Section G–address this possibility. This is because our data is based

on innovations reported by a formal authority (e.g., the USPTO for our

baseline results) that, in essence, should deal with IP thefts. In addition,

the idea of IP theft should be less of a concern in our specification given that

our country-year fixed effects should control for IP protection intensity in

each country at an aggregate level (though they do not control for variations

of IP protection intensity within a country for different technologies). As an

additional robustness test, we reestimate our main specification excluding

China from the sample. We do this because China is a country that (1) is

large in size and, therefore, represents an important share of migrants and

(2) is known to have weaker intellectual property protection. Our results are

robust to the exclusion of China and are documented in Online Appendix

Section F1.

5 Conclusions

In this paper, we study and provide robust econometric evidence of the role

of immigrant inventors in shaping innovation dynamics in their receiving

countries. In particular, our analysis shows that–controlling for other means

of exchange such as trade and FDI–countries receiving immigrant inventors

from other nations that specialize in patenting in technology p are more likely

to have important increases in patent applications in that same technology.

Our estimates imply that a twofold increase in the number of inventor

33

immigrants can explain an increase of 25 to 60 percent in the likelihood of

gaining technological advantage in the same technology in which the inven-

tors’ home countries specialize. In our sample, this number can be as low

as 25 inventors for the average country, with a standard deviation of about

135. Our econometric analysis includes the use of IVs as well as a number of

falsification tests to rule out our results being driven by spurious correlations

or other alternative factors for which we did not account.

This paper fills a gap in the literature and explores some of our previous

work on the role migrants play in facilitating the transfer of knowledge across

borders (Bahar and Rapoport, 2018; Choudhury, 2016; Choudhury and Kim,

2018). Specifically, it examines a particular channel through which inven-

tor migrants–a small and very particular subset of high-skilled migrants–can

heavily influence innovation dynamics in their receiving countries.

By providing robust results of how migration affects the transfer of spe-

cific technologies across borders, from the home country of the migrant to the

host country, using a large number of countries, studied over two decade-long

periods, our study contributes to the literature on migrants and innovation

(e.g., Kerr, 2008; Agrawal et al., 2008; Hunt and Gauthier-Loiselle, 2010;

Kerr and Lincoln, 2010; Freeman and Huang, 2015; Ganguli, 2015; Bosetti et al.,

2015; Choudhury, 2016; Akcigit et al., 2017; Breschi et al., 2017; Bernstein et al.,

2018; Miguélez, 2018; Choudhury and Kim, 2018; Doran and Yoon, 2019).

More broadly, our findings indicate that migrant inventors can play an im-

portant role in shaping the patent production function in their host countries.

Arguably, these dynamics driven by migrant inventors play an important role

in improving other economic outcomes that follow patenting and innovation,

such as productivity and, ultimately, economic growth. Hence, this study is

another piece of evidence that the overall medium- to long-term economic

gains from migration are large and persistent over time.

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Figure 1: Patent applications by year (USPTO, EPO, and PCT)

02

00

00

04

00

00

06

00

00

08

00

00

01

.0e

+0

6

1990 2000 2010

USPTO EPO PCT

This figure presents the total number of patent applications to the USPTO, EPO, andPCT in years 1990, 2000, and 2010 around the globe.

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Figure 2: Patent applications and granted (USPTO), by year

02

00

00

04

00

00

06

00

00

08

00

00

01

.0e

+0

6

1990 2000 2010

Applications Granted

This figure presents the total number of patent applications and patents granted in years1990, 2000, and 2010 around the globe, based on the records of the USPTO.

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Figure 3: Patent application take-offs, raw data

0.0

2.0

4.0

6P

(Ta

ke

−O

ff)

/ C

AG

R

1990−2000 2000−2010

Below Median Above Median Below Median Above Median

Prob. Technology Take−Off (10 years)

Technology Growth Rate (CGAR, 10 years)

This figure presents the average probability of a patent technology take-off and the averageCAGR (using patent applications) for country-technology pairs with a stock of immigrantinventors from countries that specialize in that same technology (e.g., file patents in thattechnology subclass with an RTA above 1) below and above the sample median. The stockof immigrants is scaled by population size of the receiving country. The figure is based onsimple averages, with no controls whatsoever.

42

Figure 4: OLS vs. 2SLS estimators (Technology Take-offs)

Imm

igra

nt

Inve

nto

rsE

mig

ran

tIn

ve

nto

rs

−.01 0 .01 .02 .03

OLS 2SLS (IV1) 2SLS (IV2)

This figure plots the point estimates and their corresponding 95% confidence intervals(represented by whiskers) of the OLS and two different 2SLS estimations for both βIMandβIM , based on the results presented in Panel A of Table 2.

43

Figure 5: Correlations between real and random-inventor migrants

05

10

15

20

De

nsity

0 .2 .4 .6Correlation Coefficient

(a) Random Model 1

02

04

06

0D

en

sity

−.02 −.01 0 .01 .02Correlation Coefficient

(b) Random Model 2

This figure plots the kernel distributions between real and random inventors based on500 iterations. The left panel is based on Random Model 1, which generates a randomnumber maintaining the original distributional characteristics of the actual variable; theright panel is based on Random Model 2, which generates a random vector of migrantinventors, from 0 to 1, without any data restrictions whatsoever.

44

Figure 6: Summary of 500 estimations using random inventor figures (OLS)

−.0

05

0.0

05

.01

Iterations

Real Random

(a) Immigrant Inventors

−.0

05

0.0

05

.01

Iterations

Real Random

(b) Emigrant Inventors

Random Model 1

−.0

1−

.005

0.0

05

.01

.015

Iterations

Real Random

(c) Immigrant Inventors

−.0

1−

.005

0.0

05

.01

.015

Iterations

Real Random

(d) Emigrant Inventors

Random Model 2

This figure plots the estimators of βIM (left panel) and βEM (right panel) when substi-tuting the real number of migrant inventors between countries with a random one, foreach of 500 iterations. The results are based on an OLS estimation that includes bothregressors simultaneously. The upper row is based on a randomization approach such thatthe real and the random number of inventors have the same sample mean and distribution.The lower panel is based on a randomization approach that replaces the actual number ofinventors with a random number, with no restrictions whatsoever, distributed uniformlyfrom 0 to 1. The figure also includes, for reference, the estimators using the actual numberof migrant inventors, reported in our main results, and marked with a diamond-shapedsymbol. Whiskers represent 95% confidence intervals, based on SE clustered at the countrylevel.

45

Table 1: Summary StatisticsVariable N Mean sd Min Max

Panel A - Take-off sample (RTAc,p,to = 0)

Take-off technology (patent applications) 105,304 0.022 0.15 0.0 1.0Immigrant inventors (weighted) 105,304 24.245 135.54 0.0 8,863.0Emigrant inventors (weighted) 105,304 79.775 411.80 0.0 7,624.0Total FDI (billion USD, weighted) 105,304 32.504 216.92 0.0 9,966.9Total trade (billion USD, weighted) 105,304 106.827 258.35 0.0 4,835.1Total immigrants lagged IV (thous.,weighted) 105,304 69.439 328.88 0.0 8,701.1Total emigrants lagged IV (thous.,weighted) 105,304 141.678 486.31 0.0 8,753.7Immigrant inv. push-pull IV (weighted) 105,304 17.727 91.16 0.0 6,871.3Emigrant inv. push-pull IV (weighted) 105,304 55.170 214.60 0.0 4,387.0Panel B - Growth sample (patentsc,p,to > 0)

CAGR technology (patent applications) 18,386 0.007 0.08 -0.3 0.8Baseline patent apps 18,386 16.447 119.13 1.0 9,730.0Immigrant inventors (weighted) 18,386 709.735 2,116.58 0.0 26,642.0Emigrant inventors (weighted) 18,386 610.600 1,138.10 0.0 8,229.0Total FDI (billion USD, weighted) 18,386 836.282 1,567.10 0.0 14,487.1Total trade (billion USD, weighted) 18,386 1,109.598 1,379.50 0.0 12,273.9Total immigrants lagged IV (thous.,weighted) 18,386 499.439 903.89 0.0 8,708.0Total emigrants lagged IV (thous.,weighted) 18,386 448.861 705.99 0.0 8,699.5Immigrant inv. push-pull IV (weighted) 18,386 468.506 1,150.29 0.0 12,917.9Emigrant inv. push-pull IV (weighted) 18,386 402.796 674.82 0.0 4,975.3

This table presents the sample summary statistics for the variables used in the paper. Theupper panel presents the sample used in the estimations of technology take-offs, where welimit the sample to those country-technology observations that have no patents granted inthe beginning of the 1990-2000 and 2000-2010 periods). The lower panel presents resultsused in the estimations of patent growth regressions, where we limit our observations tothose country-technology pairs with number of patents above zero at the beginning of the1990-2000 and 2000-2010 periods.

46

Table 2: Migrant inventors and patent applications take-offs and growthPanel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0050 0.0050 0.0111 0.0135 0.0098 0.0111

(0.001)*** (0.001)*** (0.004)*** (0.005)** (0.002)*** (0.003)***Emigrant inventors 0.0017 0.0000 0.0059 -0.0029 0.0098 -0.0026

(0.001)*** (0.001) (0.003)** (0.003) (0.002)*** (0.004)Total FDI -0.0001 -0.0001 -0.0001 -0.0003 -0.0002 -0.0002 -0.0002 -0.0004 -0.0002

(0.000) (0.000) (0.000) (0.000)** (0.000)* (0.000) (0.000)*** (0.000)*** (0.000)Total Trade 0.0002 0.0003 0.0002 -0.0003 -0.0002 -0.0001 -0.0002 -0.0007 -0.0000

(0.000) (0.000)** (0.000) (0.000) (0.000) (0.000) (0.000)* (0.000)** (0.000)

N 105304 105304 105304 105304 105304 105304 105304 105304 105304r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09KP F Stat 60.59 37.42 40.91 754.69 99.14 26.79Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0045 0.0034 0.0243 0.0092 -0.0203 -0.0070

(0.001)*** (0.001)*** (0.010)** (0.010) (0.048) (0.054)Emigrant inventors 0.0042 0.0029 0.0250 0.0198 0.0253 0.0241

(0.001)*** (0.001)** (0.016) (0.021) (0.029) (0.034)Total FDI 0.0003 0.0003 0.0003 0.0002 0.0000 0.0000 0.0006 0.0000 0.0001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.001)Total Trade 0.0027 0.0021 0.0016 -0.0039 -0.0081 -0.0086 0.0109 -0.0082 -0.0053

(0.002) (0.001) (0.001) (0.003) (0.008) (0.008) (0.018) (0.014) (0.032)Baseline patent apps, log -0.0178 -0.0176 -0.0178 -0.0186 -0.0173 -0.0178 -0.0169 -0.0173 -0.0171

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)***Previous Exports Growth -0.1423 -0.1436 -0.1424 -0.1351 -0.1419 -0.1390 -0.1513 -0.1419 -0.1446

(0.039)*** (0.039)*** (0.039)*** (0.041)*** (0.040)*** (0.038)*** (0.042)*** (0.039)*** (0.047)***Zero Exports in t-1 -0.0015 -0.0013 -0.0014 -0.0016 -0.0005 -0.0008 -0.0014 -0.0005 -0.0005

(0.004) (0.004) (0.004) (0.004) (0.004) (0.003) (0.004) (0.004) (0.004)

N 18349 18349 18349 18349 18349 18349 18349 18349 18349r2 0.55 0.55 0.55 0.53 0.53 0.53 0.52 0.53 0.53KP F Stat 8.53 5.83 7.59 0.40 2.50 0.25

This table presents results of the estimation of Specification (1). Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS regressions. Columns 4-6 use as instrument

the 30-year lagged stock of migrants; and columns 7-9 use as instrument the predicted stock of migrant inventors based on push and pull factors. Panel A presents results for take-offs

in patenting applications (limiting the sample to cases where the initial patent applications for that country-technology pair was zero), while Panel B estimates future CAGR in patent

applications for country-technology pairs that already had some patenting recorded in the baseline year. All specifications include country-year and technology-year fixed effects. SEs

clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

47

Table 3: Migrant inventors and patent applications, falsification test 1Panel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors -0.0054 -0.0048 -0.0057 -0.0018 -0.0112 -0.0117

(0.002)** (0.002)** (0.009) (0.013) (0.006)* (0.007)*Emigrant inventors -0.0028 -0.0022 -0.0050 -0.0040 -0.0018 0.0011

(0.001)** (0.001) (0.007) (0.010) (0.004) (0.004)Total FDI 0.0003 0.0004 0.0004 0.0003 0.0004 0.0004 0.0003 0.0004 0.0003

(0.000)** (0.000)*** (0.000)** (0.000)** (0.000)** (0.000)* (0.000)** (0.000)** (0.000)**Total Trade -0.0007 -0.0008 0.0016 -0.0005 0.0018 0.0017 0.0030 -0.0020 0.0021

(0.003) (0.004) (0.003) (0.007) (0.009) (0.009) (0.004) (0.005) (0.005)

N 105304 105304 105304 105304 105304 105304 105304 105304 105304r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09KP F Stat 6.56 5.52 5.59 110.58 66.37 101.29Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors -0.0084 -0.0068 -0.0169 -0.0272 -0.1088 -0.0537

(0.002)*** (0.002)*** (0.009)* (0.011)** (0.024)*** (0.108)Emigrant inventors -0.0065 -0.0056 -0.0027 0.0164 -0.0455 -0.0242

(0.002)*** (0.002)*** (0.010) (0.013) (0.015)*** (0.048)Total FDI 0.0001 0.0001 0.0001 0.0001 0.0000 0.0000 0.0007 0.0004 0.0006

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)Total Trade -0.0047 -0.0041 -0.0001 0.0013 -0.0079 -0.0080 0.0669 0.0357 0.0524

(0.005) (0.005) (0.005) (0.010) (0.014) (0.013) (0.021)*** (0.022) (0.030)*Baseline patent apps, log -0.0180 -0.0183 -0.0184 -0.0182 -0.0180 -0.0172 -0.0205 -0.0213 -0.0210

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)***Previous Exports Growth -0.1491 -0.1464 -0.1489 -0.1523 -0.1461 -0.1553 -0.1869 -0.1485 -0.1675

(0.038)*** (0.040)*** (0.038)*** (0.034)*** (0.040)*** (0.033)*** (0.027)*** (0.037)*** (0.056)***Zero Exports in t-1 -0.0008 -0.0012 -0.0008 -0.0004 -0.0012 -0.0002 0.0046 -0.0003 0.0021

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.003) (0.004) (0.007)

N 18349 18349 18349 18349 18349 18349 18349 18349 18349r2 0.55 0.55 0.55 0.55 0.55 0.52 0.32 0.48 0.47KP F Stat 42.33 12.48 7.61 9.98 13.54 0.23

This table presents results of the estimation of Specification (1), with a slight modification. Instead of including in the RHS the number of migrant inventors from and in countries that

specialize in each technology in the baseline year, we use the number of migrant inventors from and in countries with zero patent applications in each technology at the beginning of the

period. Columns 1-3 show OLS estimations, while columns 4-6 show results for 2SLS regressions. Panel A presents results for take-offs in patenting applications (limiting the sample

to cases where the initial patent applications for that country-technology pair were zero), while Panel B estimates future CAGR in patent applications for country-technology pairs that

already had some patenting recorded in the baseline year. All specifications include country-year and technology-year fixed effects. SEs clustered at the country level are presented in

parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

48

Table 4: Migrant inventors and patent applications, subsample analysis

OLS 2SLS (IV1) 2SLS (IV2)

N βim βem βim βem βim βemAll Observations 105304 0.005*** -0.000 0.013** -0.004 0.011*** -0.003Non OECD 76695 0.006*** 0.000 0.015** -0.004 0.021*** -0.001OECD 28609 0.003 0.000 0.015** -0.003 0.007** -0.005Period 1990-2000 54042 0.000 0.000 0.007 -0.003 0.002* -0.000Period 2000-2010 51262 0.007*** -0.000 0.014** -0.004 0.017*** -0.005Chemistry; Metallurgy 13564 0.009*** 0.001 0.016 -0.005 0.012** 0.001Electricity 7571 0.005 0.002 0.008 0.000 0.017*** -0.003Fixed Constructions 4985 -0.000 0.002 0.016 -0.010 0.005 0.003Human Necessities 13211 0.009*** -0.001 0.012 -0.006 0.031*** -0.024Mechanical Engineering 15958 0.004 -0.002 0.018 -0.005 0.019** -0.008Performing Operations; Transporting 27529 0.001 -0.003 0.011 -0.006 0.007 -0.014Physics 12788 0.006* 0.001 0.023 -0.005 0.016*** -0.004Textiles; Paper 6663 0.003* -0.001 0.010 -0.006 0.012*** -0.006

This table summarizes OLS regressions for different cuts of the sample. The reported beta coefficients are standardized to have zero mean

and unit standard deviation, for comparison purposes. The dependent variable in all specifications is technology take-off. Significance

levels reported based on SE clustered at the country level

49

Online Appendix for

Migration inventors and the technological

advantage of nations

Dany Bahar, Prithwiraj Choudhury, and Hillel Rapoport

January 21, 2020

A Extended summary statistics

Table A1 presents summary statistics for each of the 95 countries in the

sample. The table presents the average probability of take-off for each

country (presented in percentage format) based on observations of country-

technology pairs with no patent applications in the baseline year for each

period. Similarly, the growth rate (also presented in percentage format)

is based on observations of country-technology pairs with more than zero

patent applications in the baseline period. The * symbol next to a country

name implies that the country is part of the OECD prior to the first period

studied (e.g., it does not count countries as OECD members if they became

so during the 1990s or 2000s).

[Table A1 about here.]

1

Table A1: Summary statistics by country1990-2000 2000-2010 1990-2000 2000-2010

Country Take off CAGR Take Off CAGR Country Take off CAGR Take Off CAGR

United Arab Emirates 0.00 -3.99 5.54 -5.88 South Korea 1.39 10.24 5.58 6.77Argentina 0.32 -3.46 7.03 -3.50 Kuwait 0.00 -6.70 6.45 -3.33Armenia 0.00 . 2.01 -4.34 Lebanon 0.00 -6.70 1.71 -4.13Australia * 0.82 1.10 6.92 0.92 Liechtenstein 0.32 -3.72 2.12 -5.34Austria * 0.51 -0.04 7.90 0.41 Sri Lanka 0.00 -6.09 1.56 -5.86Belgium * 1.16 0.82 7.84 0.54 Lithuania 0.00 . 2.79 -2.66Bulgaria 0.00 -6.16 3.13 -2.47 Luxembourg * 0.49 -4.64 5.53 -3.67Bosnia and Herzegovina 0.00 . 1.08 -6.70 Latvia 0.00 . 2.32 0.84Belarus 0.15 . 2.02 -2.96 Morocco 0.00 -6.70 1.10 -5.58Bermuda 0.00 -6.70 0.47 -7.11 Monaco 0.16 -4.69 1.60 -5.78Brazil 0.84 -1.76 10.36 0.62 Moldova 0.00 -6.70 1.69 -6.70Canada * 0.55 3.17 10.14 0.94 Mexico 0.85 -3.15 10.17 -2.80Switzerland * 1.30 0.00 2.16 0.21 Macedonia, FYR 0.00 . 0.00 -6.70Chile 0.31 -5.68 6.55 -2.70 Malta 0.00 0.00 1.54 -7.93China 2.16 2.36 15.42 15.21 Mongolia 0.00 -6.70 0.31 .Colombia 0.00 -3.15 4.91 -3.42 Malaysia 0.47 0.30 9.79 -0.13Costa Rica 0.00 -7.31 1.40 -4.29 Nigeria 0.00 -6.70 0.15 -2.04Cuba 0.31 . 0.31 -3.44 Netherlands * 1.29 0.57 8.37 1.08Cayman Islands 0.00 -6.70 2.32 -5.02 Norway * 1.14 -0.94 7.18 -1.12Cyprus 0.00 -6.70 3.56 -3.92 New Zealand * 0.52 -1.44 6.02 -1.23Czech Republic 0.48 -4.77 8.96 -0.94 Pakistan 0.00 -6.70 3.10 -6.70Germany * 1.02 3.44 1.25 0.40 Panama 0.00 -6.70 0.93 -6.70Denmark * 1.30 0.06 6.07 -0.59 Peru 0.00 -7.59 2.79 -6.70Algeria 0.00 -6.70 1.08 -6.70 Philippines 0.79 -5.35 5.56 -4.13Ecuador 0.00 -6.70 0.62 -6.70 Poland 0.16 -3.75 10.07 -1.36Egypt 0.00 -6.70 5.38 -4.09 North Korea 0.00 -6.70 0.46 -6.70Spain * 1.76 -0.21 10.44 1.37 Portugal * 0.47 -4.36 6.48 -1.29Estonia 0.00 . 5.15 -0.62 Russian Federation 2.39 -2.36 8.11 -1.76Finland * 1.89 1.24 5.52 -1.37 Saudi Arabia 0.31 -1.99 12.97 3.97France * 0.00 0.77 5.96 0.81 Singapore 1.91 4.87 7.64 0.15United Kingdom * 0.00 1.51 3.40 0.63 El Salvador 0.00 . 0.00 -6.70Georgia 0.00 . 1.54 -6.70 Slovakia 0.77 . 4.18 -4.48Greece * 0.31 -5.60 6.08 -1.61 Slovenia 0.92 . 3.91 -2.37Guatemala 0.00 -6.70 0.46 -7.93 Sweden * 1.35 2.73 4.74 -1.63Hong Kong 1.36 1.60 7.35 -0.95 Seychelles 0.00 -6.70 0.77 .Croatia 0.61 . 4.11 -3.53 Thailand 0.47 -1.90 6.81 -4.04Hungary 0.00 -4.43 7.00 -1.97 Trinidad and Tobago 0.00 -6.70 2.48 -7.23Indonesia 0.00 -4.02 3.50 -3.80 Tunisia 0.00 -6.70 1.55 -2.23India 1.15 4.39 14.42 5.68 Turkey * 0.00 -5.21 8.32 -1.16Ireland * 0.35 -0.74 6.69 0.18 Ukraine 0.77 . 5.52 -3.63Iran 0.00 -6.70 8.83 -4.14 Uruguay 0.00 -6.70 1.39 -6.89Iceland * 0.77 -4.46 3.06 -3.39 United States * 0.00 3.05 3.03 -0.04Israel 1.62 3.15 3.10 0.72 Uzbekistan 0.00 . 0.15 -6.70Italy * 0.79 0.98 7.01 0.21 Venezuela 0.00 -3.86 1.17 -5.12Jamaica 0.00 -6.70 0.93 -6.70 South Africa 0.00 -3.26 5.27 -3.31Jordan 0.00 -6.70 2.15 -6.70 Zimbabwe 0.00 . 0.15 -6.70Japan * 0.00 2.72 4.48 0.37 . . . .Kazakhstan 0.00 . 1.24 -6.70 . . . .Kenya 0.00 -4.46 0.78 -3.26 . . . .

The table presents values for the probability of take-off and the growth rate (CAGR), both in percentage terms, averaged across all technology subclasses for each country in each

period in our sample. Consistent with our analysis, the table presents the average probability of take-off for each country based on observations of country-technology pairs with

no patent applications in the baseline year for each period. Similarly, the growth rate is based on observations of country-technology pairs with more than zero patent applications

in the baseline period. The * symbol indicates whether the country is an OECD member (based on the 1980 cutoff, prior to the first period of analysis in our exercise).

2

B Explanatory power of bilateral migration on fu-

ture bilateral inventor stocks

As noted in the paper, one plausible concern about our identification strategy

of instrumenting current stocks of migrant inventors with our instruments

is that the first-stage correlation of the 2SLS estimation is artificially driven

up by the weighting scheme we use in our baseline specification. However,

this is not the case, as we show in the following exercise; we estimate the

following specification using data for years 1990 and 2000:

inventorsc,c′,t = βIV IVc,c′,t + γc + γc′ + θt + εc,c′,t

Where c and c′ are countries; t is year; inventorsc,c′,t is the stock of

migrant inventors from country c′ in country c at time t; and IVc,c′,t is one

of the two IVs we used for our identification strategy: the stock of total

migrants from country c′ in country c at time t-30 (that is, for years 1960

and 1970), as well as the predicted stock of inventors using push-pull factors.

Both terms in the regression are transformed using the inverse hyperbolic

sine and, thus, βIV can be interpreted as an elasticity. γc and γc′ are receiving

and sending country fixed effects, respectively; θt represents year fixed effects.

The last term represents the error.

The estimation for this specification is presented in Table B1. The ta-

ble reports the estimation of βIV for each instrument using three different

estimations that vary with the inclusion of fixed effects, as well as standard

errors in parenthesis and t-stats below them for the purpose of this exercise.

Columns 1 and 4 include only year fixed effects; Columns 2 and 5 include year

fixed effects as well as sending country and receiving country fixed effects;

Columns 3 and 6 include sending country and year (combined) fixed effects

as well as receiving country-by-year fixed effects. Columns 1 to 3 use our

first instrumental variable, the lagged stock of migrants; Columns 4 to 6 use

the predicted number of inventor migrants. In all columns, we see that there

is a positive elasticity and, perhaps more importantly, a strong explanatory

power with t-stats. Thus, we conclude that the success of our first stage is

3

not driven by mechanics due to the transformation of the variables.

[Table B1 about here.]

4

Table B1: Explanatory power of lagged migration on inventor migrantsDependent variable: Stock of Immigrant Inventors

est1 est2 est3 est4 est5 est6Immigrant stock (t-30) 0.0754 0.0362 0.0356

(0.016)*** (0.007)*** (0.007)***4.701 5.168 5.126

Immigrant inventors (Push-Pull prediction) 0.8446 0.8363 0.8374(0.044)*** (0.038)*** (0.036)***

19.024 22.053 23.028Constant -0.0704 0.0254 0.0268 -0.0100 -0.0088 -0.0089

(0.020)*** (0.017) (0.017) (0.002)*** (0.006) (0.006)-3.451 1.476 1.573 -4.019 -1.498 -1.583

N 42924 42924 42924 42924 42924 42924Adj. R2 0.16 0.35 0.41 0.72 0.74 0.74θt Y Y - Y Y -γc, γc′ N Y - N Y -γc × θt, γc′ × θt - - Y - - Y

This table estimates the elasticity of the stock of migrant inventors to the stock of total migrants 30 years before, using bilateral figures. Both left-hand side

and right-hand side variables are transformed using the inverse hyperbolic sine (asinh). Columns 1 and 4 include only year fixed effects; columns 2 and 5 include

receiving and sending country fixed effects, as well as year fixed effects; columns 3 and 6 include receiving country and year (combined) as well as sending

country and year (combined) fixed effects. The estimation uses years 1990 and 2000 as baseline years. Standard errors are clustered at the receiving country

and sending country levels and are presented in parenthesis. t-stats are presented below the standard errors∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

5

C Alternative methods for dependent binary vari-

ables

As noted, our dependent variable measuring technology take-offs is a bi-

nary variable. Hence, our OLS estimation represents a linear probability

model (LPM). We believe that given the computational difficulties posed

by estimating nonlinear models using high-dimensional fixed effects, LPM

is a reasonable choice. However, for robustness purposes, we reestimate our

main specification using the complementary log-log estimator. This par-

ticular maximum likelihood estimator, used to estimate models where the

dependent variable responds to a binary distribution, is a more advisable

option than logit or probit if the probability of take-off is small, as in our

case (Singer and Willett, 2009). For computational reasons, to limit the

number of simultaneous fixed effects, we estimate this c-log-log model for

the 2000 to 2010 decade only, focusing only on take-offs, naturally. Table

C1 presents the results, which are robust to the ones presented in the main

body of the paper.

[Table C1 about here.]

Alternatively, we also consider the approach of Horrace and Oaxaca (2006)

for binary data. The approach suggests, after the first estimation of a linear

probability model, dropping from the sample the observations for which the

predicted value falls out of the unit interval and using this subsample to

reestimate the linear probability model. Horrace and Oaxaca (2006) show

that this approach may reduce the potential biases of the linear probability

models. Table C2 reports the results using this method. In fact, very few

observations in our sample have a predicted value outside of the 0 to 1 range

(less than half a percent), so the number of observations in this estimation is

very close to those in Panel A of Table 2, therefore arriving at almost iden-

tical results. This suggests the potential bias from linear probability models

is minimal, if anything.

[Table C2 about here.]

6

Table C1: Migrant inventors and patent applications take-offs, c-log-log es-timationDependent variable: Patent applications take-off (binary)

est1 est2 est3Take off patent classImmigrant inventors 0.0856 0.1001

(0.036)** (0.037)***Emigrant inventors -0.0221 -0.0580

(0.045) (0.047)Total FDI 0.0056 0.0067 0.0069

(0.010) (0.010) (0.010)Total Trade 0.0220 0.0461 0.0342

(0.023) (0.023)** (0.024)

N 41221 41221 41221r2_p

This table presents results of the estimation of Specification (1), focusing on take-offs

(a binary dependent variable), using a (maximum likelihood) c-log-log estimator. All

specifications include country and technology fixed effects. SEs clustered at the country

level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

7

Table C2: Migrant inventors and patent applications take-offs, HO (2006)

Dependent variable: Patent applications take-off (binary)

est1 est2 est3Immigrant inventors 0.0050 0.0050

(0.001)*** (0.001)***Emigrant inventors 0.0017 0.0000

(0.001)*** (0.001)Total FDI -0.0001 -0.0001 -0.0001

(0.000) (0.000) (0.000)Total Trade 0.0002 0.0003 0.0002

(0.000) (0.000)** (0.000)

N 104792 104792 104792r2 0.09 0.09 0.09

This table presents results of the estimation of Specification (1), focusing on take-offs

(a binary dependent variable), using the methodology suggested by Horrace and Oaxaca

(2016). All specifications include country and technology fixed effects. SEs clustered at

the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

8

D Alternative dependent variables

For robustness purposes, we extend our exercise to include alternative take-

off and growth measures as our dependent variable.

Table D1 replicates the estimation shown in Panel A of Table 2, this time

using a modified version of take-offs that does not rely on RTA thresholds.

The measure uses a much simpler way to account for new technology sub-

classes in a given country-technology pair based on zero or larger than zero

patent applications. In this case, the dependent variable is measured as:

Yc,p,t→T = 1 if patentsc,p,t = 0 and patentsc,p,T ≥ 1 ,

where T − t = 10.

[Table D1 about here.]

The results in Table D1 are qualitatively consistent with those in Panel A

of Table 2, with larger point estimates, as is expected. The 2SLS results using

the first set of instruments is much noisier (thus, we are unable to find results

that are statistically significant), whereas the estimations using the second

set of instrument show results consistent with our main estimation. Note that

when using these results (instead of the RTA-based measure as our dependent

variable), we are unable to conclude anything about gaining technological

advantage in certain technology subclasses due to inventor migrants.

We also replicate our main specification using an alternative measure of

growth that includes those observations that start off with zero patents. This

measure is the symmetric percentage change and is defined as:

Yc,p,t→T =patentsc,p,T − patentsc,p,t

0.5 ∗ (patentsc,p,T + patentsc,p,t)×

1

T − t

where T − t = 10. In a sense, this growth measure allows us to include

all observations, as opposed to CAGR, since we can include those country-

technology pairs for which patentsc,p,t = 0 as their symmetric percentage

change (SPC) is defined. This measure also presents a symmetric growth

rate so that a change from patentsc,p,t = x1 to patentsc,p,T = x2 represents

9

the same percentage change (in absolute value) than from patentsc,p,T = x2

to patentsc,p,t = x1. Table D2 presents the results using all the sample

(i.e., including those country-technology pairs that start off with zero patent

applications).

[Table D2 about here.]

The OLS results of Table D2 show that when using this alternative de-

pendent variable, we find results consistent with our main estimations: Im-

migrant inventors explain faster growth rates in patent applications in the

same specialized technologies of their origin countries. The 2SLS results are

somewhat weaker in terms of statistical significance for the first set of in-

struments, but not for the second set. However, across the board, the point

estimates do hold, particularly for the sample of country-technology pairs

that start off with no patent applications whatsoever (i.e., the same sample

used in Panel A of Table 2.

10

Table D1: Migrant inventors and patent applications new technology subclassDependent variable: patentsc,p,t+10 > 0|patentsc,p,t = 0 (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0318 0.0298 0.0178 0.0160 0.0867 0.0862

(0.004)*** (0.004)*** (0.011) (0.015) (0.005)*** (0.008)***Emigrant inventors 0.0155 0.0054 0.0128 0.0023 0.0975 0.0010

(0.003)*** (0.002)*** (0.008) (0.010) (0.013)*** (0.015)Total FDI 0.0003 0.0005 0.0002 0.0006 0.0006 0.0006 -0.0009 -0.0026 -0.0010

(0.000) (0.000)* (0.000) (0.000)* (0.000) (0.000) (0.000)*** (0.001)*** (0.000)*Total Trade 0.0028 0.0034 0.0023 0.0040 0.0038 0.0038 -0.0016 -0.0073 -0.0017

(0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)*** (0.001)** (0.002)*** (0.001)

N 105304 105304 105304 105304 105304 105304 105304 105304 105304r2 0.26 0.25 0.26 0.26 0.25 0.26 0.24 0.22 0.24KP F Stat 60.59 37.42 40.91 754.69 99.14 26.79

This table presents results of the estimation of Specification (1). Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS regressions. Columns 4-6 use as

instrument the 30-year lagged stock of migrants; and columns 7-9 use as instrument the predicted stock of migrant inventors based on push and pull factors. Panel A presents results

for take-offs in patenting applications (limiting the sample to cases where the initial patent applications for that country-technology pair were zero), while Panel B estimates future

CAGR in patent applications for country-technology pairs that already had some patenting recorded in the baseline year. All specifications include country-year and technology-year

fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

11

Table D2: Migrant inventors and patent applications growth (SPC)Dependent variable: Patents application growth (SPC), yearly average

OLS 2SLS (IV1) 2SLS (IV2)

All patentsc,p,to > 0 All patentsc,p,to > 0 All patentsc,p,to > 0

Immigrant inventors 0.0048 0.0053 -0.0005 0.0034 0.0144 0.0155(0.001)*** (0.001)*** (0.004) (0.003) (0.002)*** (0.001)***

Emigrant inventors 0.0007 0.0009 0.0038 0.0001 0.0003 0.0003(0.000) (0.000)** (0.003) (0.002) (0.004) (0.003)

Total FDI 0.0000 0.0000 0.0000 0.0001 -0.0002 -0.0002(0.000) (0.000) (0.000) (0.000) (0.000)* (0.000)*

Total Trade 0.0006 0.0004 0.0007 0.0007 -0.0003 -0.0003(0.000)*** (0.000)*** (0.000)** (0.000)*** (0.000) (0.000)

Baseline patent apps, log 0.0066 0.0000 0.0073 0.0000 0.0052 0.0000(0.002)*** (.) (0.002)*** (.) (0.003)** (.)

Previous Exports Log-Growth -0.6119 0.0000 -0.6122 -0.1843 -0.6129 -0.1633(0.026)*** (.) (0.026)*** (0.013)*** (0.027)*** (0.011)***

Zero Exports in t-1 0.0813 -0.0360 0.0805 0.0000 0.0840 0.0000(0.005)*** (0.002)*** (0.005)*** (.) (0.006)*** (.)

N 123690 105304 123690 105304 123690 105304r2 0.30 0.27 0.30 0.27 0.30 0.26KP F Stat 44.26 41.07 20.71 26.81

This table presents results of the estimation of Specification (1). Columns 1-2 show OLS estimations, while columns 3-6 show results for 2SLS regressions. Columns

3-4 use as instrument the 30-year lagged stock of migrants; and columns 5-6 use as instrument the predicted stock of migrant inventors based on push and pull

factors. The table reports estimations for an alternative measure for decade-long growth in patenting applications: the symmetric percentage change. This measure

of growth is defined for country-technology pairs that start off with zero patent applications in the baseline year. Columns 1, 3, and 5 use all the sample, while

columns 2, 4, and 6 limit the sample to those observations with initial value 0 in their patent applications count, similarly to Panel A of Table 2. All specifications

include country-year and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

12

E Excluding controls for trade and FDI

Table E1 presents results of our main specification excluding controls for FDI

and trade, as some might regard these as "bad controls" since the presence

of migrant inventors could also explain an increased flow in trade and FDI.

Our main results are robust to this exercise, and the point estimates remain

fairly similar.

[Table E1 about here.]

13

Table E1: Migrant inventors and patent applications take-offs and growth, excl. controlsPanel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0049 0.0049 0.0089 0.0130 0.0086 0.0110

(0.001)*** (0.001)*** (0.003)*** (0.005)** (0.002)*** (0.002)***Emigrant inventors 0.0019 -0.0001 0.0051 -0.0037 0.0063 -0.0032

(0.001)*** (0.001) (0.002)*** (0.003) (0.001)*** (0.003)

N 105304 105304 105304 105304 105304 105304 105304 105304 105304r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09KP F Stat 61.15 63.00 39.02 1432.04 504.58 71.31Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0059 0.0038 0.0208 0.0055 0.0678 -0.0615

(0.001)*** (0.001)*** (0.008)*** (0.011) (0.065) (0.313)Emigrant inventors 0.0056 0.0039 0.0162 0.0126 0.0160 0.0286

(0.001)*** (0.002)** (0.007)** (0.014) (0.008)* (0.063)Baseline patent apps, log -0.0179 -0.0176 -0.0178 -0.0185 -0.0175 -0.0177 -0.0202 -0.0175 -0.0151

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.003)*** (0.002)*** (0.013)Previous Exports Growth -0.1424 -0.1438 -0.1424 -0.1354 -0.1413 -0.1396 -0.1133 -0.1414 -0.1673

(0.039)*** (0.039)*** (0.039)*** (0.041)*** (0.040)*** (0.037)*** (0.035)*** (0.039)*** (0.127)Zero Exports in t-1 -0.0016 -0.0014 -0.0014 -0.0015 -0.0007 -0.0009 -0.0010 -0.0008 -0.0007

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

N 18349 18349 18349 18349 18349 18349 18349 18349 18349r2 0.55 0.55 0.55 0.54 0.54 0.54 0.37 0.54 0.40KP F Stat 15.90 16.57 7.35 0.35 12.60 0.03

This table presents results of the estimation of Specification (1), excluding controls for trade and FDI. Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS

regressions. Columns 4-6 use as instrument the 30-year lagged stock of migrants; and columns 7-9 use as instrument the predicted stock of migrant inventors based on push and pull

factors. Panel A presents results for take-offs in patenting applications (limiting the sample to cases where the initial patent applications for that country-technology pair was zero), while

Panel B estimates future CAGR in patent applications for country-technology pairs that already had some patenting recorded in the baseline year. All specifications include country-year

and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

14

F Excluding China from the sample

Table F1 presents results of our main specification excluding China, as an

attempt to rule out a confounding story that attributes our results to in-

tellectual property stealing facilitated by migrant networks. We choose to

exclude China because (1) it represents an important share of migrants due

to its size and (2) it is known to have weaker intellectual property protection.

Our main results are robust to this exercise.

[Table F1 about here.]

15

Table F1: Migrant inventors and patent applications take-offs and growth, excl. ChinaPanel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0048 0.0048 0.0106 0.0126 0.0097 0.0116

(0.001)*** (0.001)*** (0.004)** (0.005)** (0.002)*** (0.003)***Emigrant inventors 0.0016 -0.0001 0.0058 -0.0025 0.0094 -0.0038

(0.001)** (0.001) (0.003)** (0.004) (0.002)*** (0.004)Total FDI -0.0001 -0.0001 -0.0001 -0.0003 -0.0002 -0.0002 -0.0002 -0.0004 -0.0001

(0.000) (0.000) (0.000) (0.000)** (0.000)* (0.000) (0.000)*** (0.000)*** (0.000)Total Trade 0.0001 0.0003 0.0002 -0.0003 -0.0002 -0.0002 -0.0003 -0.0007 0.0001

(0.000) (0.000)** (0.000) (0.000) (0.000) (0.000) (0.000)* (0.000)** (0.000)

N 104274 104274 104274 104274 104274 104274 104274 104274 104274r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09KP F Stat 60.09 37.10 40.12 732.02 95.33 24.65Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0048 0.0039 0.0210 0.0084 -0.0140 -0.0019

(0.001)*** (0.001)*** (0.009)** (0.010) (0.038) (0.046)Emigrant inventors 0.0040 0.0026 0.0214 0.0166 0.0189 0.0184

(0.001)*** (0.001)** (0.016) (0.021) (0.024) (0.032)Total FDI 0.0003 0.0003 0.0003 0.0002 0.0000 0.0000 0.0005 0.0001 0.0001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.001)Total Trade 0.0021 0.0017 0.0011 -0.0033 -0.0067 -0.0072 0.0083 -0.0055 -0.0046

(0.001) (0.001) (0.001) (0.003) (0.008) (0.008) (0.014) (0.011) (0.029)Baseline patent apps, log -0.0170 -0.0167 -0.0169 -0.0175 -0.0165 -0.0169 -0.0163 -0.0165 -0.0165

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)***Previous Exports Growth -0.1472 -0.1485 -0.1473 -0.1417 -0.1473 -0.1448 -0.1535 -0.1475 -0.1482

(0.040)*** (0.040)*** (0.040)*** (0.042)*** (0.040)*** (0.038)*** (0.042)*** (0.040)*** (0.046)***Zero Exports in t-1 -0.0014 -0.0012 -0.0013 -0.0014 -0.0005 -0.0007 -0.0014 -0.0006 -0.0007

(0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004) (0.004)

N 18077 18077 18077 18077 18077 18077 18077 18077 18077r2 0.53 0.53 0.53 0.52 0.52 0.52 0.52 0.52 0.52KP F Stat 8.22 5.74 7.34 0.46 2.41 0.20

This table presents results of the estimation of Specification (1), excluding China from our sample. Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS

regressions. Columns 4-6 use as instrument the 30-year lagged stock of migrants; and columns 7-9 use as instrument the predicted stock of migrant inventors based on push and pull

factors. Panel A presents results for take-offs in patenting applications (limiting the sample to cases where the initial patent applications for that country-technology pair was zero), while

Panel B estimates future CAGR in patent applications for country-technology pairs that already had some patenting recorded in the baseline year. All specifications include country-year

and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

16

G Using alternative patents data

Table G1 replicates the results of Tables 2, but using EPO patent applica-

tions as the main source of data, following the description in Section 2.3 (as

opposed to USPTO patent applications, used in our main results). Our main

results hold with these data.

[Table G1 about here.]

Along the same lines, Table G2 replicates the results using patent ap-

plications based on PCT records as the main source of data, following the

description in Section 2.3. Our main results hold with these data.

[Table G2 about here.]

In addition, Table G3 replicates our main results using granted patents–

as opposed to patent applications–to construct our dependent variables, as

well as those control variables that are based on patents data. In the table,

we document that our main results are robust to using granted patents as

opposed to patent applications.

We consider these results of high relevance as, even when there could be a

significant time gap between the time of patent application and the moment

it is granted, the take-offs and accelerations we document in innovation are

also present when focusing on patents that have gone through the process

imposed by patent agencies that certify the innovation.

[Table G3 about here.]

17

Table G1: Migrant inventors and patent applications take-offs and growth (EPO)Panel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0040 0.0038 0.0043 0.0008 0.0067 0.0036

(0.001)*** (0.001)*** (0.002)* (0.003) (0.001)*** (0.002)*Emigrant inventors 0.0019 0.0007 0.0052 0.0046 0.0104 0.0060

(0.001)*** (0.001) (0.002)** (0.003)* (0.002)*** (0.003)**Total FDI 0.0001 0.0002 0.0001 0.0001 0.0000 0.0000 0.0001 -0.0002 -0.0001

(0.000)** (0.000)** (0.000)* (0.000)* (0.000) (0.000) (0.000) (0.000) (0.000)Total Trade 0.0001 0.0002 0.0000 0.0001 -0.0002 -0.0002 -0.0001 -0.0009 -0.0007

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)*** (0.000)**

N 105039 105039 105039 105039 105039 105039 105039 105039 105039r2 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07KP F Stat 59.94 38.38 40.35 938.44 93.47 26.07Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0059 0.0048 0.0128 0.0047 -0.0139 -0.0377

(0.001)*** (0.001)*** (0.007)* (0.006) (0.018) (0.212)Emigrant inventors 0.0057 0.0042 0.0147 0.0118 0.0259 -0.0509

(0.002)*** (0.001)*** (0.007)** (0.007) (0.028) (0.460)Total FDI -0.0002 -0.0002 -0.0002 -0.0002 -0.0003 -0.0003 -0.0002 -0.0005 0.0004

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.005)Total Trade 0.0073 0.0059 0.0044 0.0034 -0.0018 -0.0020 0.0187 -0.0113 0.0756

(0.003)*** (0.003)** (0.003)* (0.003) (0.005) (0.005) (0.013) (0.023) (0.506)Baseline patent apps, log -0.0209 -0.0207 -0.0208 -0.0211 -0.0205 -0.0207 -0.0205 -0.0203 -0.0208

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.001)*** (0.002)*** (0.005)***Previous Exports Growth -0.1239 -0.1256 -0.1242 -0.1219 -0.1254 -0.1241 -0.1299 -0.1252 -0.1380

(0.025)*** (0.026)*** (0.025)*** (0.026)*** (0.026)*** (0.026)*** (0.027)*** (0.026)*** (0.079)*Zero Exports in t-1 -0.0006 -0.0004 -0.0004 -0.0006 0.0000 -0.0001 -0.0008 0.0006 -0.0035

(0.003) (0.003) (0.003) (0.002) (0.002) (0.002) (0.003) (0.003) (0.024)

N 18623 18623 18623 18623 18623 18623 18623 18623 18623r2 0.47 0.47 0.47 0.47 0.47 0.47 0.45 0.46 0.24KP F Stat 7.74 10.57 9.91 1.49 2.40 0.01

This table presents results of the estimation of Specification (1), using patent applications reported by the EPO to construct the dependent variable as well as patent-relevant control

variables. Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS regressions. Columns 4-6 use as instrument the 30-year lagged stock of migrants; and columns 7-9

use as instrument the predicted stock of migrant inventors based on push and pull factors. Panel A presents results for take-offs in patenting applications (limiting the sample to cases

where the initial patent applications for that country-technology pair was zero), while Panel B estimates future CAGR in patent applications for country-technology pairs that already

had some patenting recorded in the baseline year. All specifications include country-year and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

18

Table G2: Migrant inventors and patent applications take-offs and growth (PCT)Panel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0058 0.0047 0.0101 0.0075 0.0095 0.0072

(0.001)*** (0.001)*** (0.003)*** (0.004)* (0.002)*** (0.003)**Emigrant inventors 0.0044 0.0027 0.0078 0.0030 0.0131 0.0046

(0.001)*** (0.001)*** (0.003)*** (0.003) (0.003)*** (0.004)Total FDI 0.0001 0.0001 0.0000 -0.0000 -0.0000 -0.0001 -0.0000 -0.0003 -0.0001

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)** (0.000)Total Trade 0.0002 0.0001 -0.0000 -0.0001 -0.0003 -0.0002 -0.0000 -0.0008 -0.0004

(0.000)** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)*** (0.000)

N 106327 106327 106327 106327 106327 106327 106327 106327 106327r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09KP F Stat 54.56 41.29 35.09 664.82 100.84 27.92Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0057 0.0044 0.0145 0.0040 -0.0256 -0.0239

(0.002)*** (0.002)** (0.006)** (0.005) (0.023) (0.022)Emigrant inventors 0.0060 0.0046 0.0171 0.0150 0.0160 0.0068

(0.001)*** (0.001)*** (0.006)*** (0.008)* (0.019) (0.021)Total FDI 0.0003 0.0002 0.0002 0.0002 0.0000 0.0000 0.0005 0.0001 0.0004

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001)Total Trade 0.0029 0.0016 0.0006 -0.0010 -0.0053 -0.0057 0.0167 -0.0046 0.0117

(0.002) (0.002) (0.002) (0.002) (0.004) (0.004) (0.011) (0.011) (0.016)Baseline patent apps, log -0.0248 -0.0247 -0.0247 -0.0248 -0.0245 -0.0245 -0.0247 -0.0245 -0.0246

(0.003)*** (0.003)*** (0.003)*** (0.003)*** (0.003)*** (0.002)*** (0.003)*** (0.002)*** (0.003)***Previous Exports Growth -0.0684 -0.0687 -0.0692 -0.0700 -0.0711 -0.0714 -0.0629 -0.0709 -0.0647

(0.023)*** (0.023)*** (0.023)*** (0.023)*** (0.022)*** (0.022)*** (0.026)** (0.022)*** (0.024)***Zero Exports in t-1 -0.0052 -0.0052 -0.0051 -0.0047 -0.0048 -0.0047 -0.0069 -0.0048 -0.0066

(0.002)** (0.002)** (0.002)** (0.002)** (0.002)* (0.002)* (0.002)*** (0.003)* (0.002)***

N 17323 17323 17323 17323 17323 17323 17323 17323 17323r2 0.59 0.59 0.60 0.59 0.59 0.59 0.57 0.59 0.57KP F Stat 7.98 17.58 8.61 1.49 0.84 0.31

This table presents results of the estimation of Specification (1), using patent applications reported by the PCT to construct the dependent variable as well as patent-relevant control

variables. Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS regressions. Columns 4-6 use as instrument the 30-year lagged stock of migrants; and columns 7-9

use as instrument the predicted stock of migrant inventors based on push and pull factors. Panel A presents results for take-offs in patenting applications (limiting the sample to cases

where the initial patent applications for that country-technology pair was zero), while Panel B estimates future CAGR in patent applications for country-technology pairs that already

had some patenting recorded in the baseline year. All specifications include country-year and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

19

Table G3: Migrant inventors and granted patent take-offs and growth (USPTO)Panel A - Dependent variable: Patent applications take-off (binary)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0048 0.0045 0.0084 0.0090 0.0113 0.0106

(0.001)*** (0.001)*** (0.004)** (0.005)* (0.002)*** (0.002)***Emigrant inventors 0.0025 0.0009 0.0051 -0.0008 0.0135 0.0016

(0.001)*** (0.001) (0.002)** (0.003) (0.003)*** (0.004)Total FDI -0.0000 0.0000 -0.0000 -0.0001 -0.0001 -0.0001 -0.0002 -0.0004 -0.0002

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)* (0.000)*** (0.000)Total Trade 0.0001 0.0002 0.0000 -0.0002 -0.0001 -0.0001 -0.0004 -0.0012 -0.0005

(0.000) (0.000)* (0.000) (0.000) (0.000) (0.000) (0.000)*** (0.000)*** (0.000)

N 106620 106620 106620 106620 106620 106620 106620 106620 106620r2 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.08 0.09KP F Stat 60.25 37.25 41.17 738.22 95.44 26.70Panel B - Dependent variable: Patent applications growth (CAGR)

OLS 2SLS (IV1) 2SLS (IV2)

est1 est2 est3 est4 est5 est6 est7 est8 est9Immigrant inventors 0.0026 0.0012 0.0167 -0.0012 0.1321 0.1240

(0.001)** (0.001) (0.010)* (0.009) (0.163) (0.162)Emigrant inventors 0.0038 0.0033 0.0198 0.0206 0.0275 0.0184

(0.001)*** (0.002)** (0.014) (0.019) (0.034) (0.070)Total FDI 0.0004 0.0004 0.0004 0.0003 0.0001 0.0001 -0.0003 0.0000 -0.0005

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.002)Total Trade 0.0002 -0.0005 -0.0007 -0.0037 -0.0066 -0.0065 -0.0357 -0.0095 -0.0404

(0.001) (0.001) (0.001) (0.002) (0.005) (0.005) (0.046) (0.011) (0.048)Baseline patent apps, log -0.0170 -0.0168 -0.0168 -0.0175 -0.0164 -0.0164 -0.0215 -0.0163 -0.0208

(0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.002)*** (0.006)*** (0.003)*** (0.007)***Previous Exports Growth -0.1291 -0.1300 -0.1298 -0.1275 -0.1324 -0.1327 -0.1144 -0.1336 -0.1181

(0.044)*** (0.044)*** (0.044)*** (0.045)*** (0.041)*** (0.039)*** (0.048)** (0.043)*** (0.053)**Zero Exports in t-1 -0.0025 -0.0022 -0.0023 -0.0027 -0.0013 -0.0013 -0.0046 -0.0009 -0.0035

(0.005) (0.004) (0.004) (0.005) (0.004) (0.004) (0.006) (0.005) (0.008)

N 17032 17032 17032 17032 17032 17032 17032 17032 17032r2 0.50 0.50 0.50 0.49 0.49 0.49 -0.30 0.47 -0.30KP F Stat 10.74 9.75 7.46 0.35 0.50 0.35

This table presents results of the estimation of Specification (1), using granted patents by the USPTO to construct the dependent variable as well as patent-relevant control variables.

Columns 1-3 show OLS estimations, while columns 4-9 show results for 2SLS regressions. Columns 4-6 use as instrument the 30-year lagged stock of migrants; and columns 7-9 use as

instrument the predicted stock of migrant inventors based on push and pull factors. Panel A presents results for take-offs in patenting applications (limiting the sample to cases where the

initial patent applications for that country-technology pair was zero), while Panel B estimates future CAGR in patent applications for country-technology pairs that already had some

patenting recorded in the baseline year. All specifications include country-year and technology-year fixed effects. SEs clustered at the country level are presented in parenthesis.∗p < 0.10,∗∗ p < 0.05,∗∗∗ p < 0.01

20